Skin Cancer Detection using Image Processing

Abstract

Abstract: Early detection of melanoma skin cancer is crucial for effective treatment. Among various types of skin cancer, melanoma is considered the most dangerous due to its high likelihood of spreading to other parts of the body if not diagnosed and treated promptly. In recent years, non-invasive medical computer vision and medical image processing techniques have gained significant importance in clinical diagnosis. These techniques offer automated image analysis tools that enable accurate and rapid evaluation of skin lesions. The study involves several steps, including the collection of a dermo image database, preprocessing, segmentation using thresholding, and extraction of statistical features such as gray level co-occurrence matrix (GLCM), asymmetry, border color, and diameter. Feature selection is performed using principal component analysis (PCA), followed by the calculation of a total dermo copy score. The final step involves classification using convolutional neural networks (CNN). The results of the study indicate an impressive classification accuracy of 96.5%.