• Dunga Yuva Nagasai
• Pinnamraju T S Priya

There are approximately 200 different types of skin cancer, with melanoma standing out as the deadliest form among them. The diagnosis of melanoma requires a thorough and detailed evaluation, starting with a visual examination of the affected area. This initial step is performed using a high-speed camera capable of capturing highly detailed images, which are crucial for accurate assessment. The evolution of deep learning technology has significantly impacted the field of medical diagnostics by providing powerful tools to address complex challenges. In particular, deep learning models have shown remarkable potential in enhancing diagnostic accuracy. By integrating contextual photos with patient-level data, these advanced systems aim to assist dermatologists in making more accurate diagnoses. The ultimate goal is to minimize the variability in model predictions, thereby improving the reliability and effectiveness of skin cancer diagnosis and treatment.

Skin Cancer, Melanoma, Deep Learning, Convolutional Neural Network, Diagnostic Accuracy, Early Detection, Dermatology, Medical Imaging.

"Skin Cancer Prediction Website", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.g251-g261, July-2024, Available :http://www.jetir.org/papers/JETIR2407630.pdf

"Skin Cancer Prediction Website", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppg251-g261, July-2024, Available at : http://www.jetir.org/papers/JETIR2407630.pdf