Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network
Abstract Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimens...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Joshi, Prakrit [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 82(2022), 4 vom: 05. Aug., Seite 6197-6220 |
|---|---|
| Übergeordnetes Werk: |
volume:82 ; year:2022 ; number:4 ; day:05 ; month:08 ; pages:6197-6220 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-022-13412-y |
|---|
| Katalog-ID: |
OLC2133558160 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2133558160 | ||
| 003 | DE-627 | ||
| 005 | 20230506151644.0 | ||
| 007 | tu | ||
| 008 | 230506s2022 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11042-022-13412-y |2 doi | |
| 035 | |a (DE-627)OLC2133558160 | ||
| 035 | |a (DE-He213)s11042-022-13412-y-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 070 |a 004 |q VZ |
| 100 | 1 | |a Joshi, Prakrit |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 | ||
| 520 | |a Abstract Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimension reduction which result in overfitting problems. This research aims to reduce the overfitting by producing the required feature map with dimension reduction through using Convolutional Neural Network. Methodology: The proposed system uses the Enhanced Convolutional Neural Network and the autoencoder technique to increase the efficiency of feature extraction process and compresses the information. In this technique, unpooling and deconvolution is done to generate the input data to minimize the difference between input and output data. Furthermore, it extracts characteristic features from the input data set which regenerates the input data from those features by learning a network to reduce the overfitting problem. Results: Different value of accuracy and processing time is achieved using different sample group of Confocal Laser Endomicroscopy (CLE) images. Based on result, it shows that the proposed solution is better than the current system. Also, the proposed system has improved the classification accuracy by 5 ~ 5.5% in average and reduced the processing time by 20 ~ 30 milliseconds in average. Conclusion: The proposed system is focused on accurately classifying the oral cancer cells of different anatomical locations from the CLE images. Finally, this study enhances the accuracy and processing time using autoencoder method and solve the problem of overfitting. | ||
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Images classification | |
| 650 | 4 | |a Autoencoder | |
| 650 | 4 | |a Overfitting | |
| 650 | 4 | |a Oral Cancer | |
| 650 | 4 | |a Feature extraction | |
| 650 | 4 | |a Information compression | |
| 700 | 1 | |a Alsadoon, Omar Hisham |4 aut | |
| 700 | 1 | |a Alsadoon, Abeer |0 (orcid)0000-0002-2309-3540 |4 aut | |
| 700 | 1 | |a AlSallami, Nada |4 aut | |
| 700 | 1 | |a Rashid, Tarik A. |4 aut | |
| 700 | 1 | |a Prasad, P.W.C. |4 aut | |
| 700 | 1 | |a Haddad, Sami |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Multimedia tools and applications |d Springer US, 1995 |g 82(2022), 4 vom: 05. Aug., Seite 6197-6220 |w (DE-627)189064145 |w (DE-600)1287642-2 |w (DE-576)052842126 |x 1380-7501 |7 nnns |
| 773 | 1 | 8 | |g volume:82 |g year:2022 |g number:4 |g day:05 |g month:08 |g pages:6197-6220 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11042-022-13412-y |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OLC-BUB | ||
| 912 | |a SSG-OLC-MKW | ||
| 951 | |a AR | ||
| 952 | |d 82 |j 2022 |e 4 |b 05 |c 08 |h 6197-6220 | ||
| author_variant |
p j pj o h a oh oha a a aa n a na t a r ta tar p p pp s h sh |
|---|---|
| matchkey_str |
article:13807501:2022----::eperigoszadirsoeetretatoadlsiiainnrlacrn |
| hierarchy_sort_str |
2022 |
| publishDate |
2022 |
| allfields |
10.1007/s11042-022-13412-y doi (DE-627)OLC2133558160 (DE-He213)s11042-022-13412-y-p DE-627 ger DE-627 rakwb eng 070 004 VZ Joshi, Prakrit verfasserin aut Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimension reduction which result in overfitting problems. This research aims to reduce the overfitting by producing the required feature map with dimension reduction through using Convolutional Neural Network. Methodology: The proposed system uses the Enhanced Convolutional Neural Network and the autoencoder technique to increase the efficiency of feature extraction process and compresses the information. In this technique, unpooling and deconvolution is done to generate the input data to minimize the difference between input and output data. Furthermore, it extracts characteristic features from the input data set which regenerates the input data from those features by learning a network to reduce the overfitting problem. Results: Different value of accuracy and processing time is achieved using different sample group of Confocal Laser Endomicroscopy (CLE) images. Based on result, it shows that the proposed solution is better than the current system. Also, the proposed system has improved the classification accuracy by 5 ~ 5.5% in average and reduced the processing time by 20 ~ 30 milliseconds in average. Conclusion: The proposed system is focused on accurately classifying the oral cancer cells of different anatomical locations from the CLE images. Finally, this study enhances the accuracy and processing time using autoencoder method and solve the problem of overfitting. Deep learning Images classification Autoencoder Overfitting Oral Cancer Feature extraction Information compression Alsadoon, Omar Hisham aut Alsadoon, Abeer (orcid)0000-0002-2309-3540 aut AlSallami, Nada aut Rashid, Tarik A. aut Prasad, P.W.C. aut Haddad, Sami aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 4 vom: 05. Aug., Seite 6197-6220 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:4 day:05 month:08 pages:6197-6220 https://doi.org/10.1007/s11042-022-13412-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 4 05 08 6197-6220 |
| spelling |
10.1007/s11042-022-13412-y doi (DE-627)OLC2133558160 (DE-He213)s11042-022-13412-y-p DE-627 ger DE-627 rakwb eng 070 004 VZ Joshi, Prakrit verfasserin aut Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimension reduction which result in overfitting problems. This research aims to reduce the overfitting by producing the required feature map with dimension reduction through using Convolutional Neural Network. Methodology: The proposed system uses the Enhanced Convolutional Neural Network and the autoencoder technique to increase the efficiency of feature extraction process and compresses the information. In this technique, unpooling and deconvolution is done to generate the input data to minimize the difference between input and output data. Furthermore, it extracts characteristic features from the input data set which regenerates the input data from those features by learning a network to reduce the overfitting problem. Results: Different value of accuracy and processing time is achieved using different sample group of Confocal Laser Endomicroscopy (CLE) images. Based on result, it shows that the proposed solution is better than the current system. Also, the proposed system has improved the classification accuracy by 5 ~ 5.5% in average and reduced the processing time by 20 ~ 30 milliseconds in average. Conclusion: The proposed system is focused on accurately classifying the oral cancer cells of different anatomical locations from the CLE images. Finally, this study enhances the accuracy and processing time using autoencoder method and solve the problem of overfitting. Deep learning Images classification Autoencoder Overfitting Oral Cancer Feature extraction Information compression Alsadoon, Omar Hisham aut Alsadoon, Abeer (orcid)0000-0002-2309-3540 aut AlSallami, Nada aut Rashid, Tarik A. aut Prasad, P.W.C. aut Haddad, Sami aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 4 vom: 05. Aug., Seite 6197-6220 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:4 day:05 month:08 pages:6197-6220 https://doi.org/10.1007/s11042-022-13412-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 4 05 08 6197-6220 |
| allfields_unstemmed |
10.1007/s11042-022-13412-y doi (DE-627)OLC2133558160 (DE-He213)s11042-022-13412-y-p DE-627 ger DE-627 rakwb eng 070 004 VZ Joshi, Prakrit verfasserin aut Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimension reduction which result in overfitting problems. This research aims to reduce the overfitting by producing the required feature map with dimension reduction through using Convolutional Neural Network. Methodology: The proposed system uses the Enhanced Convolutional Neural Network and the autoencoder technique to increase the efficiency of feature extraction process and compresses the information. In this technique, unpooling and deconvolution is done to generate the input data to minimize the difference between input and output data. Furthermore, it extracts characteristic features from the input data set which regenerates the input data from those features by learning a network to reduce the overfitting problem. Results: Different value of accuracy and processing time is achieved using different sample group of Confocal Laser Endomicroscopy (CLE) images. Based on result, it shows that the proposed solution is better than the current system. Also, the proposed system has improved the classification accuracy by 5 ~ 5.5% in average and reduced the processing time by 20 ~ 30 milliseconds in average. Conclusion: The proposed system is focused on accurately classifying the oral cancer cells of different anatomical locations from the CLE images. Finally, this study enhances the accuracy and processing time using autoencoder method and solve the problem of overfitting. Deep learning Images classification Autoencoder Overfitting Oral Cancer Feature extraction Information compression Alsadoon, Omar Hisham aut Alsadoon, Abeer (orcid)0000-0002-2309-3540 aut AlSallami, Nada aut Rashid, Tarik A. aut Prasad, P.W.C. aut Haddad, Sami aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 4 vom: 05. Aug., Seite 6197-6220 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:4 day:05 month:08 pages:6197-6220 https://doi.org/10.1007/s11042-022-13412-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 4 05 08 6197-6220 |
| allfieldsGer |
10.1007/s11042-022-13412-y doi (DE-627)OLC2133558160 (DE-He213)s11042-022-13412-y-p DE-627 ger DE-627 rakwb eng 070 004 VZ Joshi, Prakrit verfasserin aut Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimension reduction which result in overfitting problems. This research aims to reduce the overfitting by producing the required feature map with dimension reduction through using Convolutional Neural Network. Methodology: The proposed system uses the Enhanced Convolutional Neural Network and the autoencoder technique to increase the efficiency of feature extraction process and compresses the information. In this technique, unpooling and deconvolution is done to generate the input data to minimize the difference between input and output data. Furthermore, it extracts characteristic features from the input data set which regenerates the input data from those features by learning a network to reduce the overfitting problem. Results: Different value of accuracy and processing time is achieved using different sample group of Confocal Laser Endomicroscopy (CLE) images. Based on result, it shows that the proposed solution is better than the current system. Also, the proposed system has improved the classification accuracy by 5 ~ 5.5% in average and reduced the processing time by 20 ~ 30 milliseconds in average. Conclusion: The proposed system is focused on accurately classifying the oral cancer cells of different anatomical locations from the CLE images. Finally, this study enhances the accuracy and processing time using autoencoder method and solve the problem of overfitting. Deep learning Images classification Autoencoder Overfitting Oral Cancer Feature extraction Information compression Alsadoon, Omar Hisham aut Alsadoon, Abeer (orcid)0000-0002-2309-3540 aut AlSallami, Nada aut Rashid, Tarik A. aut Prasad, P.W.C. aut Haddad, Sami aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 4 vom: 05. Aug., Seite 6197-6220 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:4 day:05 month:08 pages:6197-6220 https://doi.org/10.1007/s11042-022-13412-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 4 05 08 6197-6220 |
| allfieldsSound |
10.1007/s11042-022-13412-y doi (DE-627)OLC2133558160 (DE-He213)s11042-022-13412-y-p DE-627 ger DE-627 rakwb eng 070 004 VZ Joshi, Prakrit verfasserin aut Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimension reduction which result in overfitting problems. This research aims to reduce the overfitting by producing the required feature map with dimension reduction through using Convolutional Neural Network. Methodology: The proposed system uses the Enhanced Convolutional Neural Network and the autoencoder technique to increase the efficiency of feature extraction process and compresses the information. In this technique, unpooling and deconvolution is done to generate the input data to minimize the difference between input and output data. Furthermore, it extracts characteristic features from the input data set which regenerates the input data from those features by learning a network to reduce the overfitting problem. Results: Different value of accuracy and processing time is achieved using different sample group of Confocal Laser Endomicroscopy (CLE) images. Based on result, it shows that the proposed solution is better than the current system. Also, the proposed system has improved the classification accuracy by 5 ~ 5.5% in average and reduced the processing time by 20 ~ 30 milliseconds in average. Conclusion: The proposed system is focused on accurately classifying the oral cancer cells of different anatomical locations from the CLE images. Finally, this study enhances the accuracy and processing time using autoencoder method and solve the problem of overfitting. Deep learning Images classification Autoencoder Overfitting Oral Cancer Feature extraction Information compression Alsadoon, Omar Hisham aut Alsadoon, Abeer (orcid)0000-0002-2309-3540 aut AlSallami, Nada aut Rashid, Tarik A. aut Prasad, P.W.C. aut Haddad, Sami aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 4 vom: 05. Aug., Seite 6197-6220 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:4 day:05 month:08 pages:6197-6220 https://doi.org/10.1007/s11042-022-13412-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 4 05 08 6197-6220 |
| language |
English |
| source |
Enthalten in Multimedia tools and applications 82(2022), 4 vom: 05. Aug., Seite 6197-6220 volume:82 year:2022 number:4 day:05 month:08 pages:6197-6220 |
| sourceStr |
Enthalten in Multimedia tools and applications 82(2022), 4 vom: 05. Aug., Seite 6197-6220 volume:82 year:2022 number:4 day:05 month:08 pages:6197-6220 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Deep learning Images classification Autoencoder Overfitting Oral Cancer Feature extraction Information compression |
| dewey-raw |
070 |
| isfreeaccess_bool |
false |
| container_title |
Multimedia tools and applications |
| authorswithroles_txt_mv |
Joshi, Prakrit @@aut@@ Alsadoon, Omar Hisham @@aut@@ Alsadoon, Abeer @@aut@@ AlSallami, Nada @@aut@@ Rashid, Tarik A. @@aut@@ Prasad, P.W.C. @@aut@@ Haddad, Sami @@aut@@ |
| publishDateDaySort_date |
2022-08-05T00:00:00Z |
| hierarchy_top_id |
189064145 |
| dewey-sort |
270 |
| id |
OLC2133558160 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2133558160</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506151644.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230506s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-022-13412-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2133558160</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11042-022-13412-y-p</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="082" ind1="0" ind2="4"><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Joshi, Prakrit</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimension reduction which result in overfitting problems. This research aims to reduce the overfitting by producing the required feature map with dimension reduction through using Convolutional Neural Network. Methodology: The proposed system uses the Enhanced Convolutional Neural Network and the autoencoder technique to increase the efficiency of feature extraction process and compresses the information. In this technique, unpooling and deconvolution is done to generate the input data to minimize the difference between input and output data. Furthermore, it extracts characteristic features from the input data set which regenerates the input data from those features by learning a network to reduce the overfitting problem. Results: Different value of accuracy and processing time is achieved using different sample group of Confocal Laser Endomicroscopy (CLE) images. Based on result, it shows that the proposed solution is better than the current system. Also, the proposed system has improved the classification accuracy by 5 ~ 5.5% in average and reduced the processing time by 20 ~ 30 milliseconds in average. Conclusion: The proposed system is focused on accurately classifying the oral cancer cells of different anatomical locations from the CLE images. Finally, this study enhances the accuracy and processing time using autoencoder method and solve the problem of overfitting.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Images classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Autoencoder</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Overfitting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Oral Cancer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature extraction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Information compression</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Alsadoon, Omar Hisham</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Alsadoon, Abeer</subfield><subfield code="0">(orcid)0000-0002-2309-3540</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">AlSallami, Nada</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rashid, Tarik A.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Prasad, P.W.C.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Haddad, Sami</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Springer US, 1995</subfield><subfield code="g">82(2022), 4 vom: 05. Aug., Seite 6197-6220</subfield><subfield code="w">(DE-627)189064145</subfield><subfield code="w">(DE-600)1287642-2</subfield><subfield code="w">(DE-576)052842126</subfield><subfield code="x">1380-7501</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:82</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:4</subfield><subfield code="g">day:05</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:6197-6220</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11042-022-13412-y</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</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_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MKW</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">82</subfield><subfield code="j">2022</subfield><subfield code="e">4</subfield><subfield code="b">05</subfield><subfield code="c">08</subfield><subfield code="h">6197-6220</subfield></datafield></record></collection>
|
| author |
Joshi, Prakrit |
| spellingShingle |
Joshi, Prakrit ddc 070 misc Deep learning misc Images classification misc Autoencoder misc Overfitting misc Oral Cancer misc Feature extraction misc Information compression Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network |
| authorStr |
Joshi, Prakrit |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)189064145 |
| format |
Article |
| dewey-ones |
070 - News media, journalism & publishing 004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1380-7501 |
| topic_title |
070 004 VZ Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network Deep learning Images classification Autoencoder Overfitting Oral Cancer Feature extraction Information compression |
| topic |
ddc 070 misc Deep learning misc Images classification misc Autoencoder misc Overfitting misc Oral Cancer misc Feature extraction misc Information compression |
| topic_unstemmed |
ddc 070 misc Deep learning misc Images classification misc Autoencoder misc Overfitting misc Oral Cancer misc Feature extraction misc Information compression |
| topic_browse |
ddc 070 misc Deep learning misc Images classification misc Autoencoder misc Overfitting misc Oral Cancer misc Feature extraction misc Information compression |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Multimedia tools and applications |
| hierarchy_parent_id |
189064145 |
| dewey-tens |
070 - News media, journalism & publishing 000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Multimedia tools and applications |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 |
| title |
Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network |
| ctrlnum |
(DE-627)OLC2133558160 (DE-He213)s11042-022-13412-y-p |
| title_full |
Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network |
| author_sort |
Joshi, Prakrit |
| journal |
Multimedia tools and applications |
| journalStr |
Multimedia tools and applications |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
txt |
| container_start_page |
6197 |
| author_browse |
Joshi, Prakrit Alsadoon, Omar Hisham Alsadoon, Abeer AlSallami, Nada Rashid, Tarik A. Prasad, P.W.C. Haddad, Sami |
| container_volume |
82 |
| class |
070 004 VZ |
| format_se |
Aufsätze |
| author-letter |
Joshi, Prakrit |
| doi_str_mv |
10.1007/s11042-022-13412-y |
| normlink |
(ORCID)0000-0002-2309-3540 |
| normlink_prefix_str_mv |
(orcid)0000-0002-2309-3540 |
| dewey-full |
070 004 |
| title_sort |
deep learning for size and microscope feature extraction and classification in oral cancer: enhanced convolution neural network |
| title_auth |
Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network |
| abstract |
Abstract Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimension reduction which result in overfitting problems. This research aims to reduce the overfitting by producing the required feature map with dimension reduction through using Convolutional Neural Network. Methodology: The proposed system uses the Enhanced Convolutional Neural Network and the autoencoder technique to increase the efficiency of feature extraction process and compresses the information. In this technique, unpooling and deconvolution is done to generate the input data to minimize the difference between input and output data. Furthermore, it extracts characteristic features from the input data set which regenerates the input data from those features by learning a network to reduce the overfitting problem. Results: Different value of accuracy and processing time is achieved using different sample group of Confocal Laser Endomicroscopy (CLE) images. Based on result, it shows that the proposed solution is better than the current system. Also, the proposed system has improved the classification accuracy by 5 ~ 5.5% in average and reduced the processing time by 20 ~ 30 milliseconds in average. Conclusion: The proposed system is focused on accurately classifying the oral cancer cells of different anatomical locations from the CLE images. Finally, this study enhances the accuracy and processing time using autoencoder method and solve the problem of overfitting. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimension reduction which result in overfitting problems. This research aims to reduce the overfitting by producing the required feature map with dimension reduction through using Convolutional Neural Network. Methodology: The proposed system uses the Enhanced Convolutional Neural Network and the autoencoder technique to increase the efficiency of feature extraction process and compresses the information. In this technique, unpooling and deconvolution is done to generate the input data to minimize the difference between input and output data. Furthermore, it extracts characteristic features from the input data set which regenerates the input data from those features by learning a network to reduce the overfitting problem. Results: Different value of accuracy and processing time is achieved using different sample group of Confocal Laser Endomicroscopy (CLE) images. Based on result, it shows that the proposed solution is better than the current system. Also, the proposed system has improved the classification accuracy by 5 ~ 5.5% in average and reduced the processing time by 20 ~ 30 milliseconds in average. Conclusion: The proposed system is focused on accurately classifying the oral cancer cells of different anatomical locations from the CLE images. Finally, this study enhances the accuracy and processing time using autoencoder method and solve the problem of overfitting. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimension reduction which result in overfitting problems. This research aims to reduce the overfitting by producing the required feature map with dimension reduction through using Convolutional Neural Network. Methodology: The proposed system uses the Enhanced Convolutional Neural Network and the autoencoder technique to increase the efficiency of feature extraction process and compresses the information. In this technique, unpooling and deconvolution is done to generate the input data to minimize the difference between input and output data. Furthermore, it extracts characteristic features from the input data set which regenerates the input data from those features by learning a network to reduce the overfitting problem. Results: Different value of accuracy and processing time is achieved using different sample group of Confocal Laser Endomicroscopy (CLE) images. Based on result, it shows that the proposed solution is better than the current system. Also, the proposed system has improved the classification accuracy by 5 ~ 5.5% in average and reduced the processing time by 20 ~ 30 milliseconds in average. Conclusion: The proposed system is focused on accurately classifying the oral cancer cells of different anatomical locations from the CLE images. Finally, this study enhances the accuracy and processing time using autoencoder method and solve the problem of overfitting. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW |
| container_issue |
4 |
| title_short |
Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network |
| url |
https://doi.org/10.1007/s11042-022-13412-y |
| remote_bool |
false |
| author2 |
Alsadoon, Omar Hisham Alsadoon, Abeer AlSallami, Nada Rashid, Tarik A. Prasad, P.W.C. Haddad, Sami |
| author2Str |
Alsadoon, Omar Hisham Alsadoon, Abeer AlSallami, Nada Rashid, Tarik A. Prasad, P.W.C. Haddad, Sami |
| ppnlink |
189064145 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11042-022-13412-y |
| up_date |
2024-07-03T20:11:17.392Z |
| _version_ |
1803590017895890944 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2133558160</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506151644.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230506s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-022-13412-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2133558160</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11042-022-13412-y-p</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="082" ind1="0" ind2="4"><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Joshi, Prakrit</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimension reduction which result in overfitting problems. This research aims to reduce the overfitting by producing the required feature map with dimension reduction through using Convolutional Neural Network. Methodology: The proposed system uses the Enhanced Convolutional Neural Network and the autoencoder technique to increase the efficiency of feature extraction process and compresses the information. In this technique, unpooling and deconvolution is done to generate the input data to minimize the difference between input and output data. Furthermore, it extracts characteristic features from the input data set which regenerates the input data from those features by learning a network to reduce the overfitting problem. Results: Different value of accuracy and processing time is achieved using different sample group of Confocal Laser Endomicroscopy (CLE) images. Based on result, it shows that the proposed solution is better than the current system. Also, the proposed system has improved the classification accuracy by 5 ~ 5.5% in average and reduced the processing time by 20 ~ 30 milliseconds in average. Conclusion: The proposed system is focused on accurately classifying the oral cancer cells of different anatomical locations from the CLE images. Finally, this study enhances the accuracy and processing time using autoencoder method and solve the problem of overfitting.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Images classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Autoencoder</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Overfitting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Oral Cancer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature extraction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Information compression</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Alsadoon, Omar Hisham</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Alsadoon, Abeer</subfield><subfield code="0">(orcid)0000-0002-2309-3540</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">AlSallami, Nada</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rashid, Tarik A.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Prasad, P.W.C.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Haddad, Sami</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Springer US, 1995</subfield><subfield code="g">82(2022), 4 vom: 05. Aug., Seite 6197-6220</subfield><subfield code="w">(DE-627)189064145</subfield><subfield code="w">(DE-600)1287642-2</subfield><subfield code="w">(DE-576)052842126</subfield><subfield code="x">1380-7501</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:82</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:4</subfield><subfield code="g">day:05</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:6197-6220</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11042-022-13412-y</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</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_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MKW</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">82</subfield><subfield code="j">2022</subfield><subfield code="e">4</subfield><subfield code="b">05</subfield><subfield code="c">08</subfield><subfield code="h">6197-6220</subfield></datafield></record></collection>
|
| score |
7.399315 |