Published Paper Details:

This study explores the integration of artificial intelligence (AI) in the early detection and diagnosis of skin cancer, with a focus on convolutional neural networks (CNNs), transfer learning, and hybrid learning methods. The use of pre-trained models such as VGG16, coupled with advanced data augmentation and optimization techniques, demonstrates significant improvement in classifying skin lesions with high accuracy. The proposed system incorporates an enhanced CNN model, a validation module to eliminate irrelevant inputs, and an appointment scheduling feature, making it a practical and scalable tool for clinical use. Mobile AI deployment and lightweight model architectures are highlighted as effective strategies for expanding access in resource-constrained environments. Furthermore, the research addresses critical challenges in clinical adoption, including algorithmic bias, data diversity, and ethical concerns such as patient privacy. This work underscores the transformative potential of AI in dermatology by enabling early diagnosis, personalized care, and expanded access to diagnostic support in underserved regions.