A Robust Convolutional Neural Network Approach for Classifying Skin Cancer

Early detection identified by dermoscopy images significantly decreases the mortality rate from skin cancer. However, the accuracy of the system diagnosis is impacted by multiple factors. A significant issue in this procedure arises during the process of acquiring images. The image quality in medical photography is frequently affected by unanticipated circumstances such as noises and brightness variations, initial digitalization, and sampling. In this work, we suggest a method to reduce the possibility of erroneous diagnosis. The proposed approach begins with preprocessing the data set through CNN and deep learning techniques for data visualization, pre-processing, and augmentation. This preprocessing aids in accelerating the rate of reorganization by eliminating all irrelevant textures. The outcomes identified a 96% accuracy rate with a 4% margin of error. This scheme demonstrated a high degree of accuracy in determining the images, as evidenced by its Precision and F1 Scores of 85% and 87%, respectively.