Machine Vision based Intelligent Breast Cancer Detection

Artificial intelligence, especially deep learning, has sparked a great deal of interest in bioinformatics, particularly complications in clinical imaging. It has achieved great success by helping the CAD system achieve high-precision results. Despite this, detecting breast cancer on mammography imag...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Nof Yasir [verfasserIn] Shahzad Anwar [verfasserIn] Muhammad Tahir Khan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Machine learning Biomedical Engineering Clinical image processing

Übergeordnetes Werk:	In: Pakistan Journal of Engineering & Technology - The University of Lahore, 2021, 5(2022), 1
Übergeordnetes Werk:	volume:5 ; year:2022 ; number:1

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.51846/vol5iss1pp1-10

Katalog-ID:	DOAJ04898311X

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ04898311X
003	DE-627
005	20230308140936.0
007	cr uuu---uuuuu
008	230227s2022 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.51846/vol5iss1pp1-10 \|2 doi
035			\|a (DE-627)DOAJ04898311X
035			\|a (DE-599)DOAJ3d0beb3b2c8541e794cfa87aae9ece63
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	0		\|a Nof Yasir \|e verfasserin \|4 aut
245	1	0	\|a Machine Vision based Intelligent Breast Cancer Detection
264		1	\|c 2022
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Artificial intelligence, especially deep learning, has sparked a great deal of interest in bioinformatics, particularly complications in clinical imaging. It has achieved great success by helping the CAD system achieve high-precision results. Despite this, detecting breast cancer on mammography images is still considered a critical challenge. The work aims to decrease FPR and FNR and increase the value of MCC. To achieve this goal, two state-of-the-art object detection models are used, YOLOv5 and Mask RCNN.YOLOv5 detects and classifies the mass as benign or malignant. Due to the spatial limitations of YOLOV5, the original model is modified to achieve the desired results. Mask RCNN detects the edges of tumours invading the breast parenchyma and also detects the size of the tumours. The size of the tumours defines the stage of cancer. The model was trained on the INbreast dataset with YOLOv5+Mask RCNN. The performance of the proposed model was evaluated compared to the original version of YOLOv5. The proposed technique achieves higher performance with a lower False-positive rate of 0.05 and False-negative rate of 0.03 and a high MCC value of 92.02%. The experiments performed show that the accuracy of YOLOv5 in combination with Mask RCNN is 0.06 higher than that of YOLOv5 alone. Additionally, this work could help determine the patient's prognosis and allow physicians to be more accurate and predictable at early-stage breast cancer detection.
650		4	\|a Machine learning
650		4	\|a Biomedical Engineering
650		4	\|a Clinical image processing
653		0	\|a Technology
653		0	\|a T
700	0		\|a Shahzad Anwar \|e verfasserin \|4 aut
700	0		\|a Muhammad Tahir Khan \|e verfasserin \|4 aut
773	0	8	\|i In \|t Pakistan Journal of Engineering & Technology \|d The University of Lahore, 2021 \|g 5(2022), 1 \|w (DE-627)1760613614 \|x 26642050 \|7 nnns
773	1	8	\|g volume:5 \|g year:2022 \|g number:1
856	4	0	\|u https://doi.org/10.51846/vol5iss1pp1-10 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/3d0beb3b2c8541e794cfa87aae9ece63 \|z kostenfrei
856	4	0	\|u https://hpej.net/journals/pakjet/article/view/1570 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2664-2042 \|y Journal toc \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2664-2050 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 5 \|j 2022 \|e 1

Indexfelder

author_variant	n y ny s a sa m t k mtk
matchkey_str	article:26642050:2022----::ahnvsobsdnelgnbesc
hierarchy_sort_str	2022
publishDate	2022
allfields	10.51846/vol5iss1pp1-10 doi (DE-627)DOAJ04898311X (DE-599)DOAJ3d0beb3b2c8541e794cfa87aae9ece63 DE-627 ger DE-627 rakwb eng Nof Yasir verfasserin aut Machine Vision based Intelligent Breast Cancer Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence, especially deep learning, has sparked a great deal of interest in bioinformatics, particularly complications in clinical imaging. It has achieved great success by helping the CAD system achieve high-precision results. Despite this, detecting breast cancer on mammography images is still considered a critical challenge. The work aims to decrease FPR and FNR and increase the value of MCC. To achieve this goal, two state-of-the-art object detection models are used, YOLOv5 and Mask RCNN.YOLOv5 detects and classifies the mass as benign or malignant. Due to the spatial limitations of YOLOV5, the original model is modified to achieve the desired results. Mask RCNN detects the edges of tumours invading the breast parenchyma and also detects the size of the tumours. The size of the tumours defines the stage of cancer. The model was trained on the INbreast dataset with YOLOv5+Mask RCNN. The performance of the proposed model was evaluated compared to the original version of YOLOv5. The proposed technique achieves higher performance with a lower False-positive rate of 0.05 and False-negative rate of 0.03 and a high MCC value of 92.02%. The experiments performed show that the accuracy of YOLOv5 in combination with Mask RCNN is 0.06 higher than that of YOLOv5 alone. Additionally, this work could help determine the patient's prognosis and allow physicians to be more accurate and predictable at early-stage breast cancer detection. Machine learning Biomedical Engineering Clinical image processing Technology T Shahzad Anwar verfasserin aut Muhammad Tahir Khan verfasserin aut In Pakistan Journal of Engineering & Technology The University of Lahore, 2021 5(2022), 1 (DE-627)1760613614 26642050 nnns volume:5 year:2022 number:1 https://doi.org/10.51846/vol5iss1pp1-10 kostenfrei https://doaj.org/article/3d0beb3b2c8541e794cfa87aae9ece63 kostenfrei https://hpej.net/journals/pakjet/article/view/1570 kostenfrei https://doaj.org/toc/2664-2042 Journal toc kostenfrei https://doaj.org/toc/2664-2050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2022 1
spelling	10.51846/vol5iss1pp1-10 doi (DE-627)DOAJ04898311X (DE-599)DOAJ3d0beb3b2c8541e794cfa87aae9ece63 DE-627 ger DE-627 rakwb eng Nof Yasir verfasserin aut Machine Vision based Intelligent Breast Cancer Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence, especially deep learning, has sparked a great deal of interest in bioinformatics, particularly complications in clinical imaging. It has achieved great success by helping the CAD system achieve high-precision results. Despite this, detecting breast cancer on mammography images is still considered a critical challenge. The work aims to decrease FPR and FNR and increase the value of MCC. To achieve this goal, two state-of-the-art object detection models are used, YOLOv5 and Mask RCNN.YOLOv5 detects and classifies the mass as benign or malignant. Due to the spatial limitations of YOLOV5, the original model is modified to achieve the desired results. Mask RCNN detects the edges of tumours invading the breast parenchyma and also detects the size of the tumours. The size of the tumours defines the stage of cancer. The model was trained on the INbreast dataset with YOLOv5+Mask RCNN. The performance of the proposed model was evaluated compared to the original version of YOLOv5. The proposed technique achieves higher performance with a lower False-positive rate of 0.05 and False-negative rate of 0.03 and a high MCC value of 92.02%. The experiments performed show that the accuracy of YOLOv5 in combination with Mask RCNN is 0.06 higher than that of YOLOv5 alone. Additionally, this work could help determine the patient's prognosis and allow physicians to be more accurate and predictable at early-stage breast cancer detection. Machine learning Biomedical Engineering Clinical image processing Technology T Shahzad Anwar verfasserin aut Muhammad Tahir Khan verfasserin aut In Pakistan Journal of Engineering & Technology The University of Lahore, 2021 5(2022), 1 (DE-627)1760613614 26642050 nnns volume:5 year:2022 number:1 https://doi.org/10.51846/vol5iss1pp1-10 kostenfrei https://doaj.org/article/3d0beb3b2c8541e794cfa87aae9ece63 kostenfrei https://hpej.net/journals/pakjet/article/view/1570 kostenfrei https://doaj.org/toc/2664-2042 Journal toc kostenfrei https://doaj.org/toc/2664-2050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2022 1
allfields_unstemmed	10.51846/vol5iss1pp1-10 doi (DE-627)DOAJ04898311X (DE-599)DOAJ3d0beb3b2c8541e794cfa87aae9ece63 DE-627 ger DE-627 rakwb eng Nof Yasir verfasserin aut Machine Vision based Intelligent Breast Cancer Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence, especially deep learning, has sparked a great deal of interest in bioinformatics, particularly complications in clinical imaging. It has achieved great success by helping the CAD system achieve high-precision results. Despite this, detecting breast cancer on mammography images is still considered a critical challenge. The work aims to decrease FPR and FNR and increase the value of MCC. To achieve this goal, two state-of-the-art object detection models are used, YOLOv5 and Mask RCNN.YOLOv5 detects and classifies the mass as benign or malignant. Due to the spatial limitations of YOLOV5, the original model is modified to achieve the desired results. Mask RCNN detects the edges of tumours invading the breast parenchyma and also detects the size of the tumours. The size of the tumours defines the stage of cancer. The model was trained on the INbreast dataset with YOLOv5+Mask RCNN. The performance of the proposed model was evaluated compared to the original version of YOLOv5. The proposed technique achieves higher performance with a lower False-positive rate of 0.05 and False-negative rate of 0.03 and a high MCC value of 92.02%. The experiments performed show that the accuracy of YOLOv5 in combination with Mask RCNN is 0.06 higher than that of YOLOv5 alone. Additionally, this work could help determine the patient's prognosis and allow physicians to be more accurate and predictable at early-stage breast cancer detection. Machine learning Biomedical Engineering Clinical image processing Technology T Shahzad Anwar verfasserin aut Muhammad Tahir Khan verfasserin aut In Pakistan Journal of Engineering & Technology The University of Lahore, 2021 5(2022), 1 (DE-627)1760613614 26642050 nnns volume:5 year:2022 number:1 https://doi.org/10.51846/vol5iss1pp1-10 kostenfrei https://doaj.org/article/3d0beb3b2c8541e794cfa87aae9ece63 kostenfrei https://hpej.net/journals/pakjet/article/view/1570 kostenfrei https://doaj.org/toc/2664-2042 Journal toc kostenfrei https://doaj.org/toc/2664-2050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2022 1
allfieldsGer	10.51846/vol5iss1pp1-10 doi (DE-627)DOAJ04898311X (DE-599)DOAJ3d0beb3b2c8541e794cfa87aae9ece63 DE-627 ger DE-627 rakwb eng Nof Yasir verfasserin aut Machine Vision based Intelligent Breast Cancer Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence, especially deep learning, has sparked a great deal of interest in bioinformatics, particularly complications in clinical imaging. It has achieved great success by helping the CAD system achieve high-precision results. Despite this, detecting breast cancer on mammography images is still considered a critical challenge. The work aims to decrease FPR and FNR and increase the value of MCC. To achieve this goal, two state-of-the-art object detection models are used, YOLOv5 and Mask RCNN.YOLOv5 detects and classifies the mass as benign or malignant. Due to the spatial limitations of YOLOV5, the original model is modified to achieve the desired results. Mask RCNN detects the edges of tumours invading the breast parenchyma and also detects the size of the tumours. The size of the tumours defines the stage of cancer. The model was trained on the INbreast dataset with YOLOv5+Mask RCNN. The performance of the proposed model was evaluated compared to the original version of YOLOv5. The proposed technique achieves higher performance with a lower False-positive rate of 0.05 and False-negative rate of 0.03 and a high MCC value of 92.02%. The experiments performed show that the accuracy of YOLOv5 in combination with Mask RCNN is 0.06 higher than that of YOLOv5 alone. Additionally, this work could help determine the patient's prognosis and allow physicians to be more accurate and predictable at early-stage breast cancer detection. Machine learning Biomedical Engineering Clinical image processing Technology T Shahzad Anwar verfasserin aut Muhammad Tahir Khan verfasserin aut In Pakistan Journal of Engineering & Technology The University of Lahore, 2021 5(2022), 1 (DE-627)1760613614 26642050 nnns volume:5 year:2022 number:1 https://doi.org/10.51846/vol5iss1pp1-10 kostenfrei https://doaj.org/article/3d0beb3b2c8541e794cfa87aae9ece63 kostenfrei https://hpej.net/journals/pakjet/article/view/1570 kostenfrei https://doaj.org/toc/2664-2042 Journal toc kostenfrei https://doaj.org/toc/2664-2050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2022 1
allfieldsSound	10.51846/vol5iss1pp1-10 doi (DE-627)DOAJ04898311X (DE-599)DOAJ3d0beb3b2c8541e794cfa87aae9ece63 DE-627 ger DE-627 rakwb eng Nof Yasir verfasserin aut Machine Vision based Intelligent Breast Cancer Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Artificial intelligence, especially deep learning, has sparked a great deal of interest in bioinformatics, particularly complications in clinical imaging. It has achieved great success by helping the CAD system achieve high-precision results. Despite this, detecting breast cancer on mammography images is still considered a critical challenge. The work aims to decrease FPR and FNR and increase the value of MCC. To achieve this goal, two state-of-the-art object detection models are used, YOLOv5 and Mask RCNN.YOLOv5 detects and classifies the mass as benign or malignant. Due to the spatial limitations of YOLOV5, the original model is modified to achieve the desired results. Mask RCNN detects the edges of tumours invading the breast parenchyma and also detects the size of the tumours. The size of the tumours defines the stage of cancer. The model was trained on the INbreast dataset with YOLOv5+Mask RCNN. The performance of the proposed model was evaluated compared to the original version of YOLOv5. The proposed technique achieves higher performance with a lower False-positive rate of 0.05 and False-negative rate of 0.03 and a high MCC value of 92.02%. The experiments performed show that the accuracy of YOLOv5 in combination with Mask RCNN is 0.06 higher than that of YOLOv5 alone. Additionally, this work could help determine the patient's prognosis and allow physicians to be more accurate and predictable at early-stage breast cancer detection. Machine learning Biomedical Engineering Clinical image processing Technology T Shahzad Anwar verfasserin aut Muhammad Tahir Khan verfasserin aut In Pakistan Journal of Engineering & Technology The University of Lahore, 2021 5(2022), 1 (DE-627)1760613614 26642050 nnns volume:5 year:2022 number:1 https://doi.org/10.51846/vol5iss1pp1-10 kostenfrei https://doaj.org/article/3d0beb3b2c8541e794cfa87aae9ece63 kostenfrei https://hpej.net/journals/pakjet/article/view/1570 kostenfrei https://doaj.org/toc/2664-2042 Journal toc kostenfrei https://doaj.org/toc/2664-2050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2022 1
language	English
source	In Pakistan Journal of Engineering & Technology 5(2022), 1 volume:5 year:2022 number:1
sourceStr	In Pakistan Journal of Engineering & Technology 5(2022), 1 volume:5 year:2022 number:1
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Machine learning Biomedical Engineering Clinical image processing Technology T
isfreeaccess_bool	true
container_title	Pakistan Journal of Engineering & Technology
authorswithroles_txt_mv	Nof Yasir @@aut@@ Shahzad Anwar @@aut@@ Muhammad Tahir Khan @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	1760613614
id	DOAJ04898311X
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ04898311X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308140936.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.51846/vol5iss1pp1-10</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ04898311X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ3d0beb3b2c8541e794cfa87aae9ece63</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Nof Yasir</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine Vision based Intelligent Breast Cancer Detection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Artificial intelligence, especially deep learning, has sparked a great deal of interest in bioinformatics, particularly complications in clinical imaging. It has achieved great success by helping the CAD system achieve high-precision results. Despite this, detecting breast cancer on mammography images is still considered a critical challenge. The work aims to decrease FPR and FNR and increase the value of MCC. To achieve this goal, two state-of-the-art object detection models are used, YOLOv5 and Mask RCNN.YOLOv5 detects and classifies the mass as benign or malignant. Due to the spatial limitations of YOLOV5, the original model is modified to achieve the desired results. Mask RCNN detects the edges of tumours invading the breast parenchyma and also detects the size of the tumours. The size of the tumours defines the stage of cancer. The model was trained on the INbreast dataset with YOLOv5+Mask RCNN. The performance of the proposed model was evaluated compared to the original version of YOLOv5. The proposed technique achieves higher performance with a lower False-positive rate of 0.05 and False-negative rate of 0.03 and a high MCC value of 92.02%. The experiments performed show that the accuracy of YOLOv5 in combination with Mask RCNN is 0.06 higher than that of YOLOv5 alone. Additionally, this work could help determine the patient's prognosis and allow physicians to be more accurate and predictable at early-stage breast cancer detection.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Biomedical Engineering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clinical image processing</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shahzad Anwar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Muhammad Tahir Khan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Pakistan Journal of Engineering & Technology</subfield><subfield code="d">The University of Lahore, 2021</subfield><subfield code="g">5(2022), 1</subfield><subfield code="w">(DE-627)1760613614</subfield><subfield code="x">26642050</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:5</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:1</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.51846/vol5iss1pp1-10</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/3d0beb3b2c8541e794cfa87aae9ece63</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://hpej.net/journals/pakjet/article/view/1570</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2664-2042</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2664-2050</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">5</subfield><subfield code="j">2022</subfield><subfield code="e">1</subfield></datafield></record></collection>
author	Nof Yasir
spellingShingle	Nof Yasir misc Machine learning misc Biomedical Engineering misc Clinical image processing misc Technology misc T Machine Vision based Intelligent Breast Cancer Detection
authorStr	Nof Yasir
ppnlink_with_tag_str_mv	@@773@@(DE-627)1760613614
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut
collection	DOAJ
remote_str	true
illustrated	Not Illustrated
issn	26642050
topic_title	Machine Vision based Intelligent Breast Cancer Detection Machine learning Biomedical Engineering Clinical image processing
topic	misc Machine learning misc Biomedical Engineering misc Clinical image processing misc Technology misc T
topic_unstemmed	misc Machine learning misc Biomedical Engineering misc Clinical image processing misc Technology misc T
topic_browse	misc Machine learning misc Biomedical Engineering misc Clinical image processing misc Technology misc T
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Pakistan Journal of Engineering & Technology
hierarchy_parent_id	1760613614
hierarchy_top_title	Pakistan Journal of Engineering & Technology
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)1760613614
title	Machine Vision based Intelligent Breast Cancer Detection
ctrlnum	(DE-627)DOAJ04898311X (DE-599)DOAJ3d0beb3b2c8541e794cfa87aae9ece63
title_full	Machine Vision based Intelligent Breast Cancer Detection
author_sort	Nof Yasir
journal	Pakistan Journal of Engineering & Technology
journalStr	Pakistan Journal of Engineering & Technology
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2022
contenttype_str_mv	txt
author_browse	Nof Yasir Shahzad Anwar Muhammad Tahir Khan
container_volume	5
format_se	Elektronische Aufsätze
author-letter	Nof Yasir
doi_str_mv	10.51846/vol5iss1pp1-10
author2-role	verfasserin
title_sort	machine vision based intelligent breast cancer detection
title_auth	Machine Vision based Intelligent Breast Cancer Detection
abstract	Artificial intelligence, especially deep learning, has sparked a great deal of interest in bioinformatics, particularly complications in clinical imaging. It has achieved great success by helping the CAD system achieve high-precision results. Despite this, detecting breast cancer on mammography images is still considered a critical challenge. The work aims to decrease FPR and FNR and increase the value of MCC. To achieve this goal, two state-of-the-art object detection models are used, YOLOv5 and Mask RCNN.YOLOv5 detects and classifies the mass as benign or malignant. Due to the spatial limitations of YOLOV5, the original model is modified to achieve the desired results. Mask RCNN detects the edges of tumours invading the breast parenchyma and also detects the size of the tumours. The size of the tumours defines the stage of cancer. The model was trained on the INbreast dataset with YOLOv5+Mask RCNN. The performance of the proposed model was evaluated compared to the original version of YOLOv5. The proposed technique achieves higher performance with a lower False-positive rate of 0.05 and False-negative rate of 0.03 and a high MCC value of 92.02%. The experiments performed show that the accuracy of YOLOv5 in combination with Mask RCNN is 0.06 higher than that of YOLOv5 alone. Additionally, this work could help determine the patient's prognosis and allow physicians to be more accurate and predictable at early-stage breast cancer detection.
abstractGer	Artificial intelligence, especially deep learning, has sparked a great deal of interest in bioinformatics, particularly complications in clinical imaging. It has achieved great success by helping the CAD system achieve high-precision results. Despite this, detecting breast cancer on mammography images is still considered a critical challenge. The work aims to decrease FPR and FNR and increase the value of MCC. To achieve this goal, two state-of-the-art object detection models are used, YOLOv5 and Mask RCNN.YOLOv5 detects and classifies the mass as benign or malignant. Due to the spatial limitations of YOLOV5, the original model is modified to achieve the desired results. Mask RCNN detects the edges of tumours invading the breast parenchyma and also detects the size of the tumours. The size of the tumours defines the stage of cancer. The model was trained on the INbreast dataset with YOLOv5+Mask RCNN. The performance of the proposed model was evaluated compared to the original version of YOLOv5. The proposed technique achieves higher performance with a lower False-positive rate of 0.05 and False-negative rate of 0.03 and a high MCC value of 92.02%. The experiments performed show that the accuracy of YOLOv5 in combination with Mask RCNN is 0.06 higher than that of YOLOv5 alone. Additionally, this work could help determine the patient's prognosis and allow physicians to be more accurate and predictable at early-stage breast cancer detection.
abstract_unstemmed	Artificial intelligence, especially deep learning, has sparked a great deal of interest in bioinformatics, particularly complications in clinical imaging. It has achieved great success by helping the CAD system achieve high-precision results. Despite this, detecting breast cancer on mammography images is still considered a critical challenge. The work aims to decrease FPR and FNR and increase the value of MCC. To achieve this goal, two state-of-the-art object detection models are used, YOLOv5 and Mask RCNN.YOLOv5 detects and classifies the mass as benign or malignant. Due to the spatial limitations of YOLOV5, the original model is modified to achieve the desired results. Mask RCNN detects the edges of tumours invading the breast parenchyma and also detects the size of the tumours. The size of the tumours defines the stage of cancer. The model was trained on the INbreast dataset with YOLOv5+Mask RCNN. The performance of the proposed model was evaluated compared to the original version of YOLOv5. The proposed technique achieves higher performance with a lower False-positive rate of 0.05 and False-negative rate of 0.03 and a high MCC value of 92.02%. The experiments performed show that the accuracy of YOLOv5 in combination with Mask RCNN is 0.06 higher than that of YOLOv5 alone. Additionally, this work could help determine the patient's prognosis and allow physicians to be more accurate and predictable at early-stage breast cancer detection.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	1
title_short	Machine Vision based Intelligent Breast Cancer Detection
url	https://doi.org/10.51846/vol5iss1pp1-10 https://doaj.org/article/3d0beb3b2c8541e794cfa87aae9ece63 https://hpej.net/journals/pakjet/article/view/1570 https://doaj.org/toc/2664-2042 https://doaj.org/toc/2664-2050
remote_bool	true
author2	Shahzad Anwar Muhammad Tahir Khan
author2Str	Shahzad Anwar Muhammad Tahir Khan
ppnlink	1760613614
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.51846/vol5iss1pp1-10
up_date	2024-07-03T20:44:46.403Z
_version_	1803592124496609280
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ04898311X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308140936.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.51846/vol5iss1pp1-10</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ04898311X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ3d0beb3b2c8541e794cfa87aae9ece63</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Nof Yasir</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine Vision based Intelligent Breast Cancer Detection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Artificial intelligence, especially deep learning, has sparked a great deal of interest in bioinformatics, particularly complications in clinical imaging. It has achieved great success by helping the CAD system achieve high-precision results. Despite this, detecting breast cancer on mammography images is still considered a critical challenge. The work aims to decrease FPR and FNR and increase the value of MCC. To achieve this goal, two state-of-the-art object detection models are used, YOLOv5 and Mask RCNN.YOLOv5 detects and classifies the mass as benign or malignant. Due to the spatial limitations of YOLOV5, the original model is modified to achieve the desired results. Mask RCNN detects the edges of tumours invading the breast parenchyma and also detects the size of the tumours. The size of the tumours defines the stage of cancer. The model was trained on the INbreast dataset with YOLOv5+Mask RCNN. The performance of the proposed model was evaluated compared to the original version of YOLOv5. The proposed technique achieves higher performance with a lower False-positive rate of 0.05 and False-negative rate of 0.03 and a high MCC value of 92.02%. The experiments performed show that the accuracy of YOLOv5 in combination with Mask RCNN is 0.06 higher than that of YOLOv5 alone. Additionally, this work could help determine the patient's prognosis and allow physicians to be more accurate and predictable at early-stage breast cancer detection.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Biomedical Engineering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clinical image processing</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shahzad Anwar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Muhammad Tahir Khan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Pakistan Journal of Engineering & Technology</subfield><subfield code="d">The University of Lahore, 2021</subfield><subfield code="g">5(2022), 1</subfield><subfield code="w">(DE-627)1760613614</subfield><subfield code="x">26642050</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:5</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:1</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.51846/vol5iss1pp1-10</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/3d0beb3b2c8541e794cfa87aae9ece63</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://hpej.net/journals/pakjet/article/view/1570</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2664-2042</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2664-2050</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">5</subfield><subfield code="j">2022</subfield><subfield code="e">1</subfield></datafield></record></collection>
score	7.4019537

Nicht das Richtige dabei?

Schreiben Sie uns!

Machine Vision based Intelligent Breast Cancer Detection

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?