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@article{202918,
        author = {Harshal Hingarh and Dr. Lalji Prasad},
        title = {Review on Machine Learning and Deep Learning Approaches for Automated Skin Cancer Diagnosis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {10368-10376},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202918},
        abstract = {Skin cancer is one of the most common cancers worldwide. Melanoma, in particular, can become life-threatening if not detected early. Accurate and timely diagnosis plays a key role in improving patient survival. In recent years, Artificial Intelligence (AI) has been widely explored as a support tool for early skin cancer detection. This study reviews recent research articles focused on machine learning and deep learning techniques for skin cancer detection. Commonly used datasets such as ISIC and HAM10000 were examined in the reviewed studies. The methods were grouped into traditional machine learning models like Support Vector Machines and Random Forests, and deep learning models like CNN, ResNet, EfficientNet, and MobileNet. This review provides a clear and structured understanding of current AI approaches in skin cancer detection. The study aims to guide researchers toward building more reliable and practical diagnostic systems.},
        keywords = {Skin Cancer Detection, Deep Learning, ResNet, Transfer Learning, Dermoscopy, ISIC Dataset, CNN},
        month = {May},
        }