Skin Cancer Detection using modified CNN with GLCME Features

Skin cancer detection is a critical application of computer vision in the medical field. Early detection of skin cancer, including melanoma and non-melanoma types, significantly improves treatment outcomes. Automated detection methods can assist dermatologists by providing consistent and objective analysis. Traditional methods rely on clinical expertise and manual examination, which can be subjective and error-prone. Machine learning is the subset of AI that offers promising results for automatic skin cancer detection using image analysis. This report explores a hybrid approach combining Convolutional Neural Networks (CNNs) with GLCM and Energy features as one set of feature to enhance diagnostic accuracy. It is standard clinical criteria for evaluating skin lesions. The combination of CNNs with GLCME features enhances diagnostic accuracy by leveraging both learned and handcrafted features.