• Sufyan Kadiwala
• Zubiya Husain
• Shahid Shaikh
• Shaid Shaikh
• Prof. Shiburaj Pappu

Skin cancer remains one of the most prevalent and potentially life-threatening conditions globally, emphasizing the need for early and accurate detection. In this study, we evaluate the performance of four distinct machine learning models—Artificial Neural Network (ANN), Convolutional Neural Network (CNN), XGBoost, and Random Forest—for skin cancer classification through image recognition. The models are trained and tested on a diverse dataset of skin lesions, encompassing both benign and malignant cases. By comparing these models using metrics such as accuracy, precision, recall, F1-score, and training time, we aim to identify the most suitable approach for future implementation. The model demonstrating the best performance in terms of classification accuracy and computational efficiency will be selected for subsequent advancements. This investigation not only identifies the optimal model but also provides valuable insights into AI-driven dermatological diagnostics.

Skin cancer detection , HAM1000, deep learning,

"Image Recognition using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.d770-d775, December-2024, Available :http://www.jetir.org/papers/JETIR2412392.pdf

"Image Recognition using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppd770-d775, December-2024, Available at : http://www.jetir.org/papers/JETIR2412392.pdf