Nonlinear Analysis in Skin Cancer Detection: Customized Convolutional Neural Networks Approach

Abstract

Skin cancer is a common and possibly fatal illness, emphasizing the critical importance of early detection for effective treatment. Convolutional Neural Networks (CNNs) have become effective methods for automating the detection of skin cancer in recent years. This paper proposed a novel approach to skin cancer detection, aiming to develop a robust classification system which will be able to differentiate between skin lesions with different types. The HAM10000 dataset contains a total of 10,015 images of different skin lesions. There are 7 different kinds of skin cancer photos in this collection, each sized 450x600 pixels with three color channels. To address class imbalance, oversampling was applied, and data augmentation was used to reduce the risk of model overfitting. Our proposed model comprised a customized CNN model, including convolutional layer, input layer, batch normalization layer, max-pooling layers and many more. Additionally, we utilized a customized MobileNet model incorporating various layers, such as dense layer, flattened layer, dropout layer, etc, to predict the disease precisely. Training optimization involved a learning rate reduction strategy using callbacks. Comprehensive model evaluation, utilizing various techniques, yielded an accuracy of 98.5% for the CNN model and 92% for the MobileNet model.