Skin Cancer Classification from Dermatoscopic Images Using Deep Learning Techniques

Skin cancer affects a large number of people and can become serious if not detected early. In many places, timely diagnosis is challenging due to a lack of specialists. Also, skin lesions often look very similar, which makes visual diagnosis tricky. This study looks at using deep learning to help with that. We trained a convolutional neural network model to classify several types of skin cancer using images from the ISIC dataset. Before training, the images were pre-processed and augmented to improve model performance. The model was tested using common metrics like accuracy, precision, recall, and ROC-AUC. The results indicate that AI can support doctors by making skin cancer detection faster and more reliable.