Authors : Dr. Hema N; M Satya Poornima; Madhurya R; Pragya; Pravallika K

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/4v499bxs

DOI : https://doi.org/10.5281/zenodo.14442595

Abstract : Skin cancer remains a critical global health concern, with over 2.1 million cases diagnosed annually, many in areas with limited access to dermatological care. Providing an accurate diagnosis on time is essential but challenging in rural and backward areas. The growth of artificial intelligence (AI) and deep learning has shown significant potential in aiding skin cancer detection and classification. This study focuses on using deep learning models like CNN for categorization of skin lesions. This review discusses different CNN architectures and methodologies, emphasizing the need for further innovation to enhance model accuracy across multiple classes, thus supporting widespread, accessible diagnostic solutions.

Keywords : AI (Artificial Intelligence), CNN (Convolutional Neural Networks), API (Application Program Interface), HAM10000 (Human Against Machine With 10000 Images) , VGG16 (Visual Geometry Group).

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