An enhanced CNN model for classifying skin cancer

Abstract

Unrepaired deoxyribonucleic acid in skin cells causes skin cancer by generating genetic abnormalities or mutations, rising day by day. Detecting and diagnosing skin cancer in its early stages is expensive and challenging, giving superior treatment options. Given the severity of these issues, researchers have generated a set of early classification techniques for skin cancer. Skin cancer is diagnosed and segregated from melanoma by looking at the symmetry, color, size, shape, and other features of lesions. While there are various computerized approaches for classifying skin lesions, convolutional neural networks (CNNs) have been demonstrated to exceed standard practices. Moreover, CNNs are a type of deep learning that has been prominent in various fields, including medical imaging. Multiple machine learning libraries have been used in this paper. Also, we have used five pre-trained models such as Inception V3, VGG-19, VGG-16, Efficient Net B7, ResNet 50 models and presented our proposed model for skin cancer classification using the HAM10000 dataset, which is an enormous skin cancer dataset. Following that, each competent model’s image detection categorization accuracy is evaluated by comparing and assessing. This research reports a maximum accuracy of 85.25% for Inception V3 models within five pre-trained models and maximum accuracy of 90.55% for our proposed model. In terms of image detection, our experimental configuration shows that our proposed model can attain the best classification accuracy rather than the other five pretrained models. Our findings are helpful in providing a comprehensive comparison and analysis of many neural networks in the categorization of skins cancer.