Computer aided early detection and classification of malignant melanoma

Shoaib Shafiq; P. W.C. Prasad; Abeer Alsadoon; Salih Ali; Amr Elchouemi

doi:10.1109/CICN.2018.8864942

Computer aided early detection and classification of malignant melanoma

Shoaib Shafiq, P. W.C. Prasad, Abeer Alsadoon, Salih Ali, Amr Elchouemi

Research output: Book chapter/Published conference paper › Conference paper › peer-review

1 Citation (Scopus)

Abstract

The diagnosis and application of Skin Cancer using Image Processing are a non-invasive technique. Currently, a lot of methods are present in the analysis and diagnosis of lesions, which provide quantitative information regarding a lesion and act as an early-warning method for it. This presented diagnosis can be used in hospitals as an alternate method for skin cancer detection and can help domain experts in reducing the time for its classification. The proposed method focuses on the classification of Skin Cancer with high accuracy by first reducing the noise from the images using Dull-Razor software, then segmenting the image using an automatic segmentation process. Important features are then extracted from the image using the GLCM and basic statistical method. The features are then fed into SVM to classify the image data. The investigation is carried on 50 normal and 50 melanoma images obtained from DermNet and ISIC archive.

Original language	English
Title of host publication	Proceedings, 2018 10th international conference on computational intelligence and communication networks
Subtitle of host publication	CICN 2018
Editors	D. M. Akbar Hussain, Geetam Singh Tomar
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	92-97
Number of pages	6
ISBN (Electronic)	9781538625774
ISBN (Print)	9781538625798 (Print on demand)
DOIs	https://doi.org/10.1109/CICN.2018.8864942
Publication status	E-pub ahead of print - 14 Oct 2019
Event	10th International Conference on Computational Intelligence and Communication Networks, CICN 2018 - Aulborg University, Esbjerg, Denmark Duration: 17 Aug 2018 → 19 Aug 2018 https://web.archive.org/web/20180402182512/http://cicn.in/ (Conference website) https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8864931 (conference welcome)

Conference

Conference	10th International Conference on Computational Intelligence and Communication Networks, CICN 2018
Country/Territory	Denmark
City	Esbjerg
Period	17/08/18 → 19/08/18
Other	The 10th International Conference on Computational Intelligence and Communication Networks (CICN 2018) is organized to address various issues to prosper the creation of intelligent solutions in future. The aim is to bring together worldwide leading researchers, developers, practitioners and educators interested in advancing the state of the art in computational intelligence and communication Networks for exchanging knowledge that encompasses a broad range of disciplines among various distinct communities. It is expected that researchers will bring new prospect for collaboration across disciplines and gain idea facilitating novel breakthrough. The theme for this conference is Innovating and Inspiring the researchers to adopt the outcome for implementation.
Internet address	https://web.archive.org/web/20180402182512/http://cicn.in/ (Conference website) https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8864931 (conference welcome)

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/CICN.2018.8864942

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Shafiq, S., Prasad, P. W. C., Alsadoon, A., Ali, S., & Elchouemi, A. (2019). Computer aided early detection and classification of malignant melanoma. In D. M. Akbar Hussain, & G. S. Tomar (Eds.), Proceedings, 2018 10th international conference on computational intelligence and communication networks: CICN 2018 (pp. 92-97). Article 8864942 IEEE, Institute of Electrical and Electronics Engineers. Advance online publication. https://doi.org/10.1109/CICN.2018.8864942

Shafiq, Shoaib ; Prasad, P. W.C. ; Alsadoon, Abeer et al. / Computer aided early detection and classification of malignant melanoma. Proceedings, 2018 10th international conference on computational intelligence and communication networks: CICN 2018. editor / D. M. Akbar Hussain ; Geetam Singh Tomar. United States : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 92-97

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title = "Computer aided early detection and classification of malignant melanoma",

abstract = "The diagnosis and application of Skin Cancer using Image Processing are a non-invasive technique. Currently, a lot of methods are present in the analysis and diagnosis of lesions, which provide quantitative information regarding a lesion and act as an early-warning method for it. This presented diagnosis can be used in hospitals as an alternate method for skin cancer detection and can help domain experts in reducing the time for its classification. The proposed method focuses on the classification of Skin Cancer with high accuracy by first reducing the noise from the images using Dull-Razor software, then segmenting the image using an automatic segmentation process. Important features are then extracted from the image using the GLCM and basic statistical method. The features are then fed into SVM to classify the image data. The investigation is carried on 50 normal and 50 melanoma images obtained from DermNet and ISIC archive.",

keywords = "Dermoscopy, Early Melanoma Detection, Melanoma Detection, Skin Cancer, Skin Lesions, Support Vector Machine (SVM)",

author = "Shoaib Shafiq and Prasad, {P. W.C.} and Abeer Alsadoon and Salih Ali and Amr Elchouemi",

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publisher = "IEEE, Institute of Electrical and Electronics Engineers",

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Shafiq, S, Prasad, PWC, Alsadoon, A, Ali, S & Elchouemi, A 2019, Computer aided early detection and classification of malignant melanoma. in DM Akbar Hussain & GS Tomar (eds), Proceedings, 2018 10th international conference on computational intelligence and communication networks: CICN 2018., 8864942, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 92-97, 10th International Conference on Computational Intelligence and Communication Networks, CICN 2018, Esbjerg, Denmark, 17/08/18. https://doi.org/10.1109/CICN.2018.8864942

Computer aided early detection and classification of malignant melanoma. / Shafiq, Shoaib; Prasad, P. W.C.; Alsadoon, Abeer et al.
Proceedings, 2018 10th international conference on computational intelligence and communication networks: CICN 2018. ed. / D. M. Akbar Hussain; Geetam Singh Tomar. United States: IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 92-97 8864942.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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T1 - Computer aided early detection and classification of malignant melanoma

AU - Shafiq, Shoaib

AU - Prasad, P. W.C.

AU - Alsadoon, Abeer

AU - Ali, Salih

AU - Elchouemi, Amr

N1 - Includes bibliographical references.

PY - 2019/10/14

Y1 - 2019/10/14

N2 - The diagnosis and application of Skin Cancer using Image Processing are a non-invasive technique. Currently, a lot of methods are present in the analysis and diagnosis of lesions, which provide quantitative information regarding a lesion and act as an early-warning method for it. This presented diagnosis can be used in hospitals as an alternate method for skin cancer detection and can help domain experts in reducing the time for its classification. The proposed method focuses on the classification of Skin Cancer with high accuracy by first reducing the noise from the images using Dull-Razor software, then segmenting the image using an automatic segmentation process. Important features are then extracted from the image using the GLCM and basic statistical method. The features are then fed into SVM to classify the image data. The investigation is carried on 50 normal and 50 melanoma images obtained from DermNet and ISIC archive.

AB - The diagnosis and application of Skin Cancer using Image Processing are a non-invasive technique. Currently, a lot of methods are present in the analysis and diagnosis of lesions, which provide quantitative information regarding a lesion and act as an early-warning method for it. This presented diagnosis can be used in hospitals as an alternate method for skin cancer detection and can help domain experts in reducing the time for its classification. The proposed method focuses on the classification of Skin Cancer with high accuracy by first reducing the noise from the images using Dull-Razor software, then segmenting the image using an automatic segmentation process. Important features are then extracted from the image using the GLCM and basic statistical method. The features are then fed into SVM to classify the image data. The investigation is carried on 50 normal and 50 melanoma images obtained from DermNet and ISIC archive.

KW - Dermoscopy

KW - Early Melanoma Detection

KW - Melanoma Detection

KW - Skin Cancer

KW - Skin Lesions

KW - Support Vector Machine (SVM)

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BT - Proceedings, 2018 10th international conference on computational intelligence and communication networks

A2 - Akbar Hussain, D. M.

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ER -

Shafiq S, Prasad PWC, Alsadoon A, Ali S, Elchouemi A. Computer aided early detection and classification of malignant melanoma. In Akbar Hussain DM, Tomar GS, editors, Proceedings, 2018 10th international conference on computational intelligence and communication networks: CICN 2018. United States: IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 92-97. 8864942 Epub 2019 Oct 14. doi: 10.1109/CICN.2018.8864942

Computer aided early detection and classification of malignant melanoma

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