Skin Cancer Classification Using DenseNet

This paper presents an innovative approach to skin cancer classification based on Dense Convolutional Network 121 (DenseNet-121), a state-of-the-art deep convolutional neural network. By automated analysis of dermatological imagery, this paper aims to enhance diagnostic precision significantly. Given a dataset of dermatological image, this study compares the performance of six leading architecture models: EfficientNet, ResNet-50, VGG16, GoogleNet, DenseNet-121, and MobileNet V3 Large. During a series of extensive training and validation phases, it has been established that DenseNet-121 is an extremely effective tool for identifying diverse types of skin cancers with high accuracy indicating a potent for early skin cancer detection and aiding in the advancement of medical diagnostic procedures.