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

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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.

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How to Cite
Shawon, M., Abedin, K. F., Majumder, A., Mahmud, A., & Mishu, M. M. C. (2021). Identification of Risk of Occurring Skin Cancer (Melanoma) Using Convolutional Neural Network (CNN). AIUB Journal of Science and Engineering (AJSE), 20(2), 47 - 51. Retrieved from
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.


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