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A Deep CNN Model for Skin Cancer Detection and Classification
(Vaclav Skala Union Agency, 2021) Junayed, Masum Shah; Anjum, Nipa; Sakib, Abu Noman Md; Islam, Md Baharul; Skala, V.; Junayed, Masum Shah, Bahçeşehir Üniversitesi, Istanbul, Turkey; Anjum, Nipa, Khulna University of Engineering and Technology, Khulna, Bangladesh; Sakib, Abu Noman Md, Khulna University of Engineering and Technology, Khulna, Bangladesh; Islam, Md Baharul, American University of Malta, Cospicua, Malta
Skin cancer is one of the most dangerous types of cancers that affect millions of people every year. The detection of skin cancer in the early stages is an expensive and challenging process. In recent studies, machine learning-based methods help dermatologists in classifying medical images. This paper proposes a deep learning-based model to detect and classify skin cancer using the concept of deep Convolution Neural Network (CNN). Initially, we collected a dataset that includes four skin cancer image data before applying them in augmentation techniques to increase the accumulated dataset size. Then, we designed a deep CNN model to train our dataset. On the test data, our model receives 95.98% accuracy that exceeds the two pre-train models, GoogleNet by 1.76% and MobileNet by 1.12%, respectively. The proposed deep CNN model also beats other contemporaneous models while being computationally comparable. © 2022 Elsevier B.V., All rights reserved.