Identification of Risk of Occurring Skin Cancer (Melanoma) Using Convolutional Neural Network (CNN)

Main Article Content

Mayinuzzaman Shawon
Kazi Fakhrul Abedin
Anik Majumder
Abir Mahmud
Md Mahbub Chowdhury Mishu


Skin cancer is one of the most common malignancy in human, has drawn attention from researchers around the world. As skin cancer can turn into fatal if not treated in its earliest stages, the necessity of devising automated skin cancer diagnosis system that can automatically detect skin cancer efficiently in its earliest stage in a faster process than traditional one is of crucial importance. In this paper, a computer aided skin cancer diagnosis system based Convolutional Neural Network method has been shown. Our proposed system consists of five stages namely image acquisition, image preprocessing, image segmentation, feature extraction and classification We remove hair any noise from the images using dull then use median filter to smoothen the images. Next, k-means algorithm was applied for image segmentation on the preprocessed images. Finally, the segmented images were fed into CNN model for feature extraction and classification. The developed system can classify benign and melanoma type skin cancers from Dermoscopic images as accurate as 80.47%. While developing the skin cancer detection system, we compare accuracy score of other models such as Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Random Forest with our proposed system. The proposed method has been tested on ‘ISIC Challenge 2016’ test dataset and an accuracy rate of 80.47% was obtained for accurately classifying benign and malignant skin lesions by our proposed model.

Article Details

How to Cite
M. Shawon, K. F. Abedin, A. Majumder, A. Mahmud, and M. M. C. Mishu, “Identification of Risk of Occurring Skin Cancer (Melanoma) Using Convolutional Neural Network (CNN)”, AJSE, vol. 20, no. 2, pp. 47 - 51, May 2021.
Author Biography

Md Mahbub Chowdhury Mishu

I am an Assistant Professor and Head of Computer Science at American International University- Bangladesh (One of the top ranked private universities in Bangladesh). My role includes teaching and developing cutting edge course contents (Lecture, Syllabus, Lesson Plan) for computer science related subjects within the faculty. Before that I worked as an Assistant Professor at Green University of Bangladesh earlier where I worked as a team member to obtain the IEB accreditation for the Computer Science department. I have several years of active teaching and research experience from the United Kingdom. I was a Lecturer of Computer Science at Northern Regional College, UK. I obtained Doctor of Philosophy (PhD) from Bournemouth University and my research area was modelling and designing of a smart/intelligent bed system that identifies the risk of bedsores/ pressure ulcer (PU) formation in the human body and prevents it in real time based on patient's physiological and body support surface (mattress) characteristics. I also worked as a researcher and Associate Lecturer at Southampton Solent University and I hold Certificate in Teaching qualification with distinction (1st Class) from University of Ulster, UK.


[1] M. C. F. Simões, J. J. S. Sousa, and A. A. C. C. Pais, “Skin cancer and new treatment perspectives: a review,” Cancer Letters, vol. 357, no. 1, pp. 8–42, 2015.
[2] T.L. Diepgen, and V. Mahler, "The epidemiology of skin cancer." British Journal of Dermatology, vol. 146, no. 1, pp. 1-6, 2002.
[3] B.K. Armstrong, and A. Kricker, “The epidemiology of UV induced skin cancer”, Journal of photochemistry and photobiology B, Biology, vol. 63, no. 1-3, pp. 8-18, October 2001.
[4] H. Kittler, H. Pehamberger, K. Wolff, and M. Binder, “Diagnostic accuracy of dermoscopy”, The lancet oncology, vol. 3, pp. 159-165, 2002.
[5] Bafounta ML, Beauchet A, Aegerter P, Saiag P., “Is dermoscopy (epiluminescence microscopy) useful for the diagnosis of melanoma? Results of a meta-analysis using techniques adapted to the evaluation of diagnostic tests”, Arch Dermatol, vol. 137, no. 10, pp. 1343-1350, 2001.
[6] G. Argenziano, H. Soyer, S. Chimenti, R. Talamini, R. Corona, F. Sera, and M. Binder, “Dermoscopy of pigmented skin lesions: Results of consensus meeting via the Internet”, Journal of the American Academy of Dermatology, vol. 48, no. 5, pp. 679–693, May 2003.
[7] S. Jaina, V. jagtapb, N. Pise, “Computer aided Melanoma skin cancer detection using Image Processing”, Procedia Computer Science, Volume 48, pp. 735-740, 2015.
[8] Mahmoud Elgamal, “Automatic Skin Cancer Images Classification”, International Journal of Advanced Computer Science and Applications,Vol. 4, No. 3, 2013.
[9] Z. Waheed, A. Waheed, M. Zafar and F. Riaz, "An efficient machine learning approach for the detection of melanoma using dermoscopic images," 2017 International Conference on Communication, Computing and Digital Systems (C-CODE), Islamabad, 2017, pp. 316-319.
[10] D. Choudhury, A. Naug and S. Ghosh, "Texture and color feature based WLS framework aided skin cancer classification using MSVM and ELM," 2015 Annual IEEE India Conference (INDICON), New Delhi, 2015, pp. 1-6.
[11] H. T. Lau and A. Al-Jumaily, "Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification," 2009 International Conference of Soft Computing and Pattern Recognition, Malacca, 2009, pp. 375-380.
[12] Esteva, A., Kuprel, B., Novoa, R. et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, pp. 115–118, January 2017.
[13] M.A. Taufiq, N. Hameed, A. Anjum, F. Hameed, “m-Skin Doctor: A Mobile Enabled System for Early Melanoma Skin Cancer Detection Using Support Vector Machine,” In: Giokas K., Bokor L., Hopfgartner F. (eds) eHealth 360°, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer, Cham, vol. 181, pp. 468-475, 2017.
[14] S. Alzahrani, W. Al-Nuaimy and B. Al-Bander, "Seven-Point Checklist with Convolutional Neural Networks for Melanoma Diagnosis," 2019 8th European Workshop on Visual Information Processing (EUVIP), Roma, Italy, 2019, pp. 211-216.
[15] B. Chakradhar, I. S. Siva Rao, V. Jhansy Archana and C. V. M. K. Hari, "Detection of Malignancy On Dermis Using J48 and Random Forest Classifiers," 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), Gunupur, India, 2020, pp. 1-6.
[17] E.‎Zagrouba‎ and‎W.‎Barhoumi,‎ “A preliminary approach for the automated recognition of malignant melanoma,” ‎Image Analysis & Stereology, vol. 23, no. 2, pp. 121-135, 2004.
[18] Khazaei Z., Ghorat F., Jarrahi A. M., Adineh H. A., Sohrabivafa M., Goodarzi E., “Global incidence and mortality of skin cancer by histological subtype and its relationship with the human development index (HDI); an ecology study in 2018,” WCRJ 2019; 6: e1265
[19] A. N. Hoshyar, A. Al-Jumaily, and R. Sulaiman, “Review on automatic early skin cancer detection,” in 2011 International Conference on Computer Science and Service System (CSSS). IEEE, 2011, pp. 4036–4039.
[20] Neoh, S.C., Srisukkham, W., Zhang, L, Todryk, S., Greystoke, B., Lim, C.P., Hossain, A. And Aslam, N., “An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images,” Scientific Reports 5 (14938), Nature, October 2015
[21] Zhang, L., Jiang, M., Farid, D. And Hossain, A.M., “Intelligent Facial Emotion Recognition and Semantic-based Topic Detection for a Humanoid Robot,” Expert Systems with Applications, Elsevier, vol. 40, no. 13, pp. 5160- 5168, October 2013.
[22] I. Goodfellow and Y. Bengio and A. Courville, Deep Learning. Cambridge, MA: The MIT Press, 2016, ch. 9.
[23] S. Albawi, T. A. Mohammed and S. Al-Zawi, "Understanding of a convolutional neural network," 2017 International Conference on Engineering and Technology (ICET), Antalya, 2017, pp. 1-6.
[24] Lee T, Ng V, Gallagher R, Coldman A, McLean D. DullRazor: a software approach to hair removal from images. Computers in Biology and Medicine, Elsivier, vol. 27, no. 6, pp. 533-43, November 1997.
[25] I. Giotis, N. Molders, S. Land, M. Biehl, M. F. Jonkman and N. Petkov,‎ “MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic‎ images,”‎ Expert Systems with Applications, Elsevier, vol. 42, no. 19, pp. 6578-6585, November 2015.
[26] Murugan, A., Nair, S.H. & Kumar, K.P.S., “Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers,” J Med Syst, vol. 43, no. 7:269, July 2019.