Abstract
Malignant melanoma is one of the most vigorous and life-threatening forms of skin cancer. Mortality rates of melanoma are quite high because it can easily proliferate to other parts of body. Despite such dreadful facts, melanoma can be cured if diagnosed at initial stage. So, it is significant to differentiate melanoma and benign lesions. For early diagnosis, CAD systems can prove worthful as they do not demand invasive procedures. In this work, methodology is proposed for classification of melanoma and non-melanoma. Initially, pre-processing steps are executed to cleanse and enhance dermoscopic images by removing undesirable objects. Then, skin lesion which is of major region of interest is obtained using active contour-based segmentation. Furthermore, colour, texture and shape features are extracted and given to SVM classifier for effective and efficient classification of melanoma and benign lesions. Experimental results are acquired using PH2 data set, and it has been seen that our proposed method yields accuracy, sensitivity and specificity of 94.5%, 82.5% and 97.5%, respectively.
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References
Medscape. https://emedicine.medscape.com/article/280245-overview
DermIS. http://skincancer.dermis.net/content/e04typesof/e154/e155/index_eng.html
JAMA Dermatology. https://jamanetwork.com/journals/jamadermatology/article-abstract/546435
American Cancer Society. https://www.cancer.org/cancer/melanoma-skin-cancer/about/key-statistics.html
Cancer.Net. https://www.cancer.net/cancer-types/melanoma/statistics
Geller, A.C., Swetter, S.M., Weinstock, M.A.: Focus on early detection to reduce melanoma deaths. J Invest. Dermatol. 135, 947–949 (2015). https://doi.org/10.1038/jid.2014.534
Takruri, M., Abubakar, A.: Bayesian decision fusion for enhancing melanoma recognition accuracy. In: International Conference on Electrical and Computing Technologies and Applications (ICECTA), pp. 1–4. IEEE Press, Ras Al Khaimah. https://doi.org/10.1109/icecta.2017.8252063 (2017)
Munia, T.T.K., Alam, M.N., Neubert, J., Fazel-Rezai, R.: Automatic diagnosis of melanoma using linear and nonlinear features from digital image. In: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4281–4284. IEEE Press, Seogwipo. https://doi.org/10.1109/embc.2017.8037802 (2017)
Fonseca-Pinto, R., Machado, M.: A textured scale-based approach to melanocytic skin lesions in dermoscopy. In: 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 279–282. IEEE Press, Opatija. https://doi.org/10.23919/mipro.2017.7973434 (2017)
Turkeli, S., Oguz, M.S., Abay, S.B., Kumbasar, T., Atay, H.T., Kurt, K.K.: A smart dermoscope design using artificial neural network. In: 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1–6. IEEE Press, Malatya. https://doi.org/10.1109/idap.2017.8090211 (2017)
Arasi, M.A., El-Horbaty, E.M., Salem, A.M., El-Dahshan, E.A.: Computational intelligence approaches for malignant melanoma detection and diagnosis. In: 2017 8th International Conference on Information Technology (ICIT), pp. 55–61. IEEE Press, Amman. https://doi.org/10.1109/icitech.2017.8079915 (2017)
Jafari, M.H., Samavi, S., Soroushmehr, S.M.R., Mohaghegh, H., Karimi, N., Najarian, K.: Set of descriptors for skin cancer diagnosis using non-dermoscopic color images. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 2638–2642. IEEE Press, Phoenix. https://doi.org/10.1109/icip.2016.7532837 (2016)
Soumya, R.S., Neethu, S., Niju, T.S., Renjini, A., Aneesh, R.P.: Advanced earlier melanoma detection algorithm using colour correlogram. In: 2016 International Conference on Communication Systems and Networks (ComNet), pp. 190–194. IEEE Press, Thiruvananthapuram. https://doi.org/10.1109/csn.2016.7824012 (2016)
Chatterjee, S., Dey, D., Munshi, S.: Mathematical morphology aided shape, texture and colour feature extraction from skin lesion for identification of malignant melanoma. In: 2015 International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), pp. 200–203. IEEE Press, Bangalore. https://doi.org/10.1109/catcon.2015.7449534 (2015)
Nezhadian, F.K., Rashidi, S.: Melanoma skin cancer detection using color and new texture feature. In: 2017 Artificial Intelligence and Signal Processing Conference (AISP), pp. 1–5. IEEE Press Shiraz. https://doi.org/10.1109/aisp.2017.8324108 (2017)
Mustafa, S., Kimura, A.: A SVM-based diagnosis of melanoma using only useful image features. In: 2018 International Workshop on Advanced Image Technology (IWAIT), pp. 1–4. IEEE Press, Chiang Mai. https://doi.org/10.1109/iwait.2018.8369646 (2018)
Mahmoud, H., Abdel-Nasser, M., Omer, O.A.: Computer aided diagnosis system for skin lesions detection using texture analysis methods. In: 2018 International Conference on Innovative Trends in Computer Engineering (ITCE), pp. 140–144. IEEE Press, Aswan. https://doi.org/10.1109/itce.2018.8327948 (2018)
Adjed, F., Safdar Gardezi, S.J., Ababsa, F., Faye, I., Chandra Dass, S.: Fusion of structural and textural features for melanoma recognition. In: IET Computer Vision, pp. 185–195. IET Press. https://doi.org/10.1049/iet-cvi.2017.0193 (2018)
Singh, S.K., Jalal, A.S.: A robust approach for automatic skin cancer disease classification. In: 2016 1st India International Conference on Information Processing (IICIP), pp. 1–4. IEEE Press, Delhi. https://doi.org/10.1109/iicip.2016.7975301 (2016)
Mendonça, T, Ferreira, P.M., Marques, J.S., Marcal, A.R, Rozeira, J.: PH2—a dermoscopic image database for research and benchmarking. In: 35th International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3–7. IEEE Press, Osaka. https://doi.org/10.1109/embc.2013.6610779 (2013)
Lee, T., Ng, V., Gallagher, R., Coldman, A., McLean, D.: DullRazor: a software approach to hair removal from images. Comput. Biol. Med. 27, 533–543 (1997)
Kolkur, S., Kalbande, D.R.: Survey of texture-based feature extraction for skin disease detection. In: 2016 International Conference on ICT in Business Industry & Government (ICTBIG), pp. 1–6. IEEE Press, Indore. https://doi.org/10.1109/ictbig.2016.7892649 (2016)
Jain, S., Jagtap, V., Pise, N.: Computer aided melanoma skin cancer detection using image processing. In: International Conference on Computer, Communication and Convergence (ICCC 2015), Procedia Computer Science, pp. 735–740. https://doi.org/10.1016/j.procs.2015.04.209 (2015)
Firmansyah, H.R., Kusumaningtyas, E.M., Hardiansyah, F.F.: Detection melanoma cancer using ABCD rule based on mobile device. In: International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), pp. 127–131. IEEE Press, Surabaya. https://doi.org/10.1109/kcic.2017.8228575 (2017)
Pathan, S., Siddalingaswamy, P.C., Lakshmi, L., Prabhu, K.G.: Classification of benign and malignant melanocytic lesions: A CAD tool. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1308–1312. IEEE Press, Udupi. https://doi.org/10.1109/icacci.2017.8126022 (2017)
Bi, L., Kim, J., Ahn, E., Feng, D., Fulham, M.: Automatic melanoma detection via multi-scale lesion-biased representation and joint reverse classification In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1055–1058. IEEE Press, Prague. https://doi.org/10.1109/isbi.2016.7493447 (2016)
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Gulati, S., Bhogal, R.K. (2020). Classification of Melanoma from Dermoscopic Images Using Machine Learning. In: Satapathy, S., Bhateja, V., Mohanty, J., Udgata, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 159. Springer, Singapore. https://doi.org/10.1007/978-981-13-9282-5_32
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