A Deep Learning Model for Skin Cancer Detection Inspection v3 And Densenet 201

Description

The most prevalent and dangerous form of cancer in people is cutaneous cancer. Skin cancer that is fatal is called melanoma. It is readily curable if caught in the early stages. The biopsy technique is the official way to diagnose melanoma. This procedure can be time-consuming and extremely painful. This research provides a computer-aided detection method for melanoma early detection. In this investigation, we did a lot of research on several deep learning-based techniques for melanoma and skin lesion cancer detection. Melanoma is a particularly dangerous type of malignant skin cancer. A thorough diagnosis of melanoma at an early stage is essential for the likelihood of a full recovery. To speed up the process of identifying skin cancer, computer vision systems can assess dermoscopy images of benign and malignant types of the disease. For this study, we experimented with neural networks using InceptionV3 and other modern deep learning-based models. Before being fed into the network, dermoscopy images are correctly processed and enhanced. We used the International Skin Imaging Collaboration (ISIC) challenge dataset to test our approaches. Our system has a model validation score of 0.81 InceptionV3, which is the greatest possible value.