Skin Cancer Detection using Resnet50

This task investigates the use of ResNet50, a profound convolutional brain organization, for the location and order of skin disease from dermoscopic pictures. Utilizing the strong element extraction capacities of ResNet50, the model is prepared on an enormous dataset of named skin sore pictures to recognize harmless and dangerous cases. By using the remaining learning structure, ResNet50 really mitigates the evaporating slope issue, empowering the model to learn profound portrayals of skin sores with further developed exactness. The proposed framework plans to help dermatologists in early conclusion by giving a solid, robotized device for skin disease discovery, possibly working on understanding results through opportune mediation. ResNet50's ability to classify various types of skin cancer with high accuracy and robustness is demonstrated by the results, highlighting its potential as a useful resource in medical imaging and diagnosis.