Detect Melanoma Skin Cancer Using an Improved Deep Learning CNN Model with Improved Computational Costs

Abstract

Melanoma cancer has been considered as one of the deadliest cancers. Melanoma is a highly malignant form of skin cancer that originates from melanocytes, the cells responsible for producing skin pigment. It is characterized by the uncontrolled prolife-elation of abnormal cells, which have the potential to invade surrounding tissues and spread to distant parts of the body. In this work, we aim to classify the skin disease into 7 classes. Our objective is to propose a deep learning CNN model to improve the accuracy of melanoma detection by customizing the number of layers in the network architecture and activation functions followed by reducing the computational cost. A comparative study is made between the improved model and the use of pre-trained Models like Resnet, Dense Net, Inception, VGG and Dense Net-II which has been giving impeccable accuracy. The HAM10000 dataset is used for research and we have got better results for the proposed model. Also, graphical results have been obtained for the same.