Classification of melanoma skin cancer using deep learning approach

Abstract

In this study, the authors propose a deep learning (DL) approach for classifying melanoma skin cancer (MSC). They introduce a convolution neural network (CNN) model that consists of 27 layers, which are carefully designed to extract features from skin lesion images and classify them into melanoma and non-melanoma classes. The proposed CNN model comprises multiple convolution layers that apply filters to the input image to extract features such as edges, shapes, and patterns. Batch normalization layers that normalize the output of the convolution layers to accelerate the learning process and prevent overfitting follow these convolution layers. The performance of the proposed CNN model was evaluated on publicly available datasets of skin lesion images, and the findings showed that it outperformed several state-of-the-art methods for melanoma classification. The authors also conducted ablation studies to analyze each layer's contribution to the model's overall performance. The proposed DL approach has the potential to assist dermatologists in the early detection of MSC, which can lead to treatment that is more effective and improves patient outcomes. It also demonstrates the effectiveness of DL techniques for medical image analysis and highlights the importance of carefully designing and optimizing CNN models for high performance. The accuracy of the proposed system is 99.99%.