Skin Cancer Detection using Image Processing

Abstract

Abstract: Early detection of melanoma, the most dangerous form of skin cancer, is crucial for effective treatment. Melanoma has a higher propensity to metastasize to other parts of the body if not identified and treated promptly. In recent years, non-invasive medical computer vision and medical image processing techniques have emerged as valuable tools in clinical diagnosis for various diseases, including melanoma. These techniques enable automatic image analysis, facilitating accurate and efficient evaluation of skin lesions. The study follows a systematic approach, involving the collection of a dermo image database and preprocessing steps such as segmentation using thresholding. Statistical feature extraction methods, including gray level cooccurrence matrix (GLCM), glam asymmetry, border analysis, color assessment, and diameter calculation, are utilized. Principal component analysis (PCA) is employed for feature selection to reduce dimensionality. A total dermo copy score is calculated, followed by classification using a convolutional neural network (CNN). The results indicate an impressive classification accuracy of 92.1%.