Skin Lesion Classification For Cancer Detection Using Convolutional Neural Network

Skin cancer is a major global health issue with millions of annual cases diagnosed. In 2023, nearly 100,000 new cases of melanoma, which is the most deadly, are estimated in the United States. For improved survival rates and appropriate treatment, early detection is critical. Conversely, conventional diagnostic techniques are long-drawn-out and at times variabe which often triggers to diagnostic-introducing delays. We propose for the first vanishing point, to enhance their VII. A CNN model is trained on a wide dataset of dermoscopic images to classify a skin lesion as cancerous or non-cancerous. Performs analysis is robust using measures of accuracy, sensitivity, specificity, and AUC The outcomes reveal that the CNN-based algorithm achieves a high level of precision exceeding previous techniques and allowing for a rapid and accurate diagnosis. This technique could become a gamechanger in skin cancer detection with early intervention and treatment and ultimately improved patient outcomes and decreased costs to the healthcare industry and society.