A CNN-Based Melanoma Skin Cancer Detection and Classification Approach

Abstract

Among various classes of skin cancer, Melanoma is a perilous pattern of skin cancer. Melanoma, widely familiar as malignant melanoma begins in cells which are called melanocytes. From ancient times, people are affected more by that right now. To overcome the complementary problem easier, need to accomplish Melanoma detection as soon as earlier. According to the keen observance and larger analysis of melanoma, CNN achieves better performance both for detection and classification efficiently, specifically deep learning feature-based Convolutional Neural Network, which has the automatic proficiency of skin cancer detection. The proposed method classifies melanoma into two classes, namely Malignant and Benign Melanoma, based on multitasking python libraries. In this research work, the process is come to an end by using the novel CNN model, working with both the training dataset at first and the testing dataset later which has been taken from kaggle platform and is publically available for 10000 images. In the report of the skin cancer image dataset, the experimental results demonstrate a higher level of accuracy rate from the image classifier.