Convolutional Neural Networks on Assembling Classification Models to Detect Melanoma Skin Cancer

In 2020, there were more than 1.2 million new skin cancer diagnoses, and melanoma was the most recurrent type of cancer. On other hand, is least common but serious form affecting both men women. This work aims to assemble classification models detect a case with high accuracy based on Convolutional Neural Networks system. The methodology considers training 21 for image classification, best assembly performance EfficientNet VGG-19 architectures, data augmentation technique used images improve its performance. results show 92.85% accuracy, 71.50% sensitivity, 94.89% specificity, an improvement 0.06% in specificity. achieved higher classification.