Melanoma Lesion Recognition using Deep Convolutional Neural Network and Global Average Pooling

Melanoma is one of the worst types of skin cancer. It appears on the skin's surface and is caused by melanocytes. The same cells are also responsible for benign lesions known as moles, which resemble melanoma in its early stages. Melanoma is frequently curable if properly treated. Much research is now focused on the automated detection of melanomas. In this paper, we present an automated melanoma identification system based on convolutional neural networks and global average pooling. When compared to other approaches on a large publicly available dataset, the experimental evaluation on the same dataset reveals that our suggested technique has high classification accuracy (98.26%), sensitivity (98.91%), and specificity (97.66%). Thus, the suggested framework may be used to help dermatologists diagnose melanoma.