An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification

Background Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced expe...
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Autor*in:	Khan, M. Attique [verfasserIn] Akram, Tallha Sharif, Muhammad Shahzad, Aamir Aurangzeb, Khursheed Alhussein, Musaed Haider, Syed Irtaza Altamrah, Abdualziz

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Image enhancement Uniform distribution Image fusion Multi-level features extraction Features fusion Features selection

Anmerkung:	© The Author(s) 2018

Übergeordnetes Werk:	Enthalten in: BMC cancer - London : BioMed Central, 2001, 18(2018), 1 vom: 05. Juni
Übergeordnetes Werk:	volume:18 ; year:2018 ; number:1 ; day:05 ; month:06

Links:	Volltext

DOI / URN:	10.1186/s12885-018-4465-8

Katalog-ID:	SPR027699595

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520			\|a Background Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency. Methods In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier. Results The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively. Conclusion The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets.
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allfields	10.1186/s12885-018-4465-8 doi (DE-627)SPR027699595 (SPR)s12885-018-4465-8-e DE-627 ger DE-627 rakwb eng Khan, M. Attique verfasserin aut An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency. Methods In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier. Results The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively. Conclusion The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets. Image enhancement (dpeaa)DE-He213 Uniform distribution (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Multi-level features extraction (dpeaa)DE-He213 Features fusion (dpeaa)DE-He213 Features selection (dpeaa)DE-He213 Akram, Tallha aut Sharif, Muhammad aut Shahzad, Aamir (orcid)0000-0003-4480-226X aut Aurangzeb, Khursheed aut Alhussein, Musaed aut Haider, Syed Irtaza aut Altamrah, Abdualziz aut Enthalten in BMC cancer London : BioMed Central, 2001 18(2018), 1 vom: 05. Juni (DE-627)326643710 (DE-600)2041352-X 1471-2407 nnns volume:18 year:2018 number:1 day:05 month:06 https://dx.doi.org/10.1186/s12885-018-4465-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2018 1 05 06
spelling	10.1186/s12885-018-4465-8 doi (DE-627)SPR027699595 (SPR)s12885-018-4465-8-e DE-627 ger DE-627 rakwb eng Khan, M. Attique verfasserin aut An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency. Methods In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier. Results The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively. Conclusion The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets. Image enhancement (dpeaa)DE-He213 Uniform distribution (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Multi-level features extraction (dpeaa)DE-He213 Features fusion (dpeaa)DE-He213 Features selection (dpeaa)DE-He213 Akram, Tallha aut Sharif, Muhammad aut Shahzad, Aamir (orcid)0000-0003-4480-226X aut Aurangzeb, Khursheed aut Alhussein, Musaed aut Haider, Syed Irtaza aut Altamrah, Abdualziz aut Enthalten in BMC cancer London : BioMed Central, 2001 18(2018), 1 vom: 05. Juni (DE-627)326643710 (DE-600)2041352-X 1471-2407 nnns volume:18 year:2018 number:1 day:05 month:06 https://dx.doi.org/10.1186/s12885-018-4465-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2018 1 05 06
allfields_unstemmed	10.1186/s12885-018-4465-8 doi (DE-627)SPR027699595 (SPR)s12885-018-4465-8-e DE-627 ger DE-627 rakwb eng Khan, M. Attique verfasserin aut An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency. Methods In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier. Results The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively. Conclusion The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets. Image enhancement (dpeaa)DE-He213 Uniform distribution (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Multi-level features extraction (dpeaa)DE-He213 Features fusion (dpeaa)DE-He213 Features selection (dpeaa)DE-He213 Akram, Tallha aut Sharif, Muhammad aut Shahzad, Aamir (orcid)0000-0003-4480-226X aut Aurangzeb, Khursheed aut Alhussein, Musaed aut Haider, Syed Irtaza aut Altamrah, Abdualziz aut Enthalten in BMC cancer London : BioMed Central, 2001 18(2018), 1 vom: 05. Juni (DE-627)326643710 (DE-600)2041352-X 1471-2407 nnns volume:18 year:2018 number:1 day:05 month:06 https://dx.doi.org/10.1186/s12885-018-4465-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2018 1 05 06
allfieldsGer	10.1186/s12885-018-4465-8 doi (DE-627)SPR027699595 (SPR)s12885-018-4465-8-e DE-627 ger DE-627 rakwb eng Khan, M. Attique verfasserin aut An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency. Methods In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier. Results The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively. Conclusion The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets. Image enhancement (dpeaa)DE-He213 Uniform distribution (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Multi-level features extraction (dpeaa)DE-He213 Features fusion (dpeaa)DE-He213 Features selection (dpeaa)DE-He213 Akram, Tallha aut Sharif, Muhammad aut Shahzad, Aamir (orcid)0000-0003-4480-226X aut Aurangzeb, Khursheed aut Alhussein, Musaed aut Haider, Syed Irtaza aut Altamrah, Abdualziz aut Enthalten in BMC cancer London : BioMed Central, 2001 18(2018), 1 vom: 05. Juni (DE-627)326643710 (DE-600)2041352-X 1471-2407 nnns volume:18 year:2018 number:1 day:05 month:06 https://dx.doi.org/10.1186/s12885-018-4465-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2018 1 05 06
allfieldsSound	10.1186/s12885-018-4465-8 doi (DE-627)SPR027699595 (SPR)s12885-018-4465-8-e DE-627 ger DE-627 rakwb eng Khan, M. Attique verfasserin aut An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency. Methods In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier. Results The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively. Conclusion The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets. Image enhancement (dpeaa)DE-He213 Uniform distribution (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Multi-level features extraction (dpeaa)DE-He213 Features fusion (dpeaa)DE-He213 Features selection (dpeaa)DE-He213 Akram, Tallha aut Sharif, Muhammad aut Shahzad, Aamir (orcid)0000-0003-4480-226X aut Aurangzeb, Khursheed aut Alhussein, Musaed aut Haider, Syed Irtaza aut Altamrah, Abdualziz aut Enthalten in BMC cancer London : BioMed Central, 2001 18(2018), 1 vom: 05. Juni (DE-627)326643710 (DE-600)2041352-X 1471-2407 nnns volume:18 year:2018 number:1 day:05 month:06 https://dx.doi.org/10.1186/s12885-018-4465-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2018 1 05 06
language	English
source	Enthalten in BMC cancer 18(2018), 1 vom: 05. Juni volume:18 year:2018 number:1 day:05 month:06
sourceStr	Enthalten in BMC cancer 18(2018), 1 vom: 05. Juni volume:18 year:2018 number:1 day:05 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Image enhancement Uniform distribution Image fusion Multi-level features extraction Features fusion Features selection
isfreeaccess_bool	true
container_title	BMC cancer
authorswithroles_txt_mv	Khan, M. Attique @@aut@@ Akram, Tallha @@aut@@ Sharif, Muhammad @@aut@@ Shahzad, Aamir @@aut@@ Aurangzeb, Khursheed @@aut@@ Alhussein, Musaed @@aut@@ Haider, Syed Irtaza @@aut@@ Altamrah, Abdualziz @@aut@@
publishDateDaySort_date	2018-06-05T00:00:00Z
hierarchy_top_id	326643710
id	SPR027699595
language_de	englisch
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Attique</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</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="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency. Methods In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier. Results The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively. Conclusion The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. 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author	Khan, M. Attique
spellingShingle	Khan, M. Attique misc Image enhancement misc Uniform distribution misc Image fusion misc Multi-level features extraction misc Features fusion misc Features selection An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification
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topic_title	An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification Image enhancement (dpeaa)DE-He213 Uniform distribution (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Multi-level features extraction (dpeaa)DE-He213 Features fusion (dpeaa)DE-He213 Features selection (dpeaa)DE-He213
topic	misc Image enhancement misc Uniform distribution misc Image fusion misc Multi-level features extraction misc Features fusion misc Features selection
topic_unstemmed	misc Image enhancement misc Uniform distribution misc Image fusion misc Multi-level features extraction misc Features fusion misc Features selection
topic_browse	misc Image enhancement misc Uniform distribution misc Image fusion misc Multi-level features extraction misc Features fusion misc Features selection
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title	An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification
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title_full	An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification
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author_browse	Khan, M. Attique Akram, Tallha Sharif, Muhammad Shahzad, Aamir Aurangzeb, Khursheed Alhussein, Musaed Haider, Syed Irtaza Altamrah, Abdualziz
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title_sort	implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification
title_auth	An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification
abstract	Background Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency. Methods In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier. Results The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively. Conclusion The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets. © The Author(s) 2018
abstractGer	Background Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency. Methods In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier. Results The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively. Conclusion The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets. © The Author(s) 2018
abstract_unstemmed	Background Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency. Methods In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier. Results The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively. Conclusion The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets. © The Author(s) 2018
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title_short	An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification
url	https://dx.doi.org/10.1186/s12885-018-4465-8
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author2	Akram, Tallha Sharif, Muhammad Shahzad, Aamir Aurangzeb, Khursheed Alhussein, Musaed Haider, Syed Irtaza Altamrah, Abdualziz
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An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification

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