Melanoma Skin Cancer Detection Using Deep Learning Techniques

Skin cancer, known as melanoma, is very dangerous in case of when it is not diagnosed at an early stage and has a positive prognosis when it is treated within the time frame. This work goes deep into the field of deep learning techniques to enhance the automation of melanoma detection. Using a dataset including 735 dermatoscopic pictures and two profound deep learning models were put to the test: A CNN model with ResNet-50 as the backbone and a Faster R-CNN model with MobileNet v3 as the backbone. The ResNet-50-powered CNN brought a precision rate of 85.06%, which proves that it is viable for accurately differentiating melanoma sores. To take identification accuracy to a higher level, the Faster R-CNN model was introduced, combining sore characterization with bounding inside the pictures. Finally, this integrated approach produced a quite impressive accuracy of 90 percent. This is good for clinical applications, as indicated by its 03% rating. From the disclosures made in the review, it is possible to infer that deep learning models, particularly those that have been augmented with district-based methods, hold the potential for early identification of melanoma. These mechanical headways can enhance patient results by integrating with adequate diagnosis and treatment, potentially saving lives and enhancing the quality of patient care in dermatology and oncology practices.