Enhancing Skin Cancer Diagnosis with Deep Learning-Based Classification

The diagnosis of skin cancer has been identified as a significant medical challenge in the 21st century due to its complexity, cost, and subjective interpretation. Early is critical, especially fatal cases like melanoma, it affects likelihood successful treatment. Therefore, there need for automated methods early diagnosis, with diverse range image samples varying diagnoses. An system dermatological disease recognition through analysis proposed compared conventional personnel-based detection. This project proposes an technique classification using images from International Skin Imaging Collaboration (ISIC) dataset, incorporating deep learning (DL) techniques that have demonstrated advancements artificial intelligence (AI) research. recognizes classifies could prove useful field, can accurately detect presence at stage. ISIC which includes vast collection various conditions, provides excellent opportunity develop validate algorithms classification. impact on industry by reducing workload personnel while providing accurate timely diagnoses..