Skin Cancer Detection Using Image Processing

Abstract

Skin Cancer is one of the most common and potentially life-threatening diseases worldwide, characterized by the abnormal growth of skin cells. This condition often arises due to prolonged exposure to harmful ultraviolet (UV) rays from sunlight. However, it can also develop in areas of the body that are not exposed to the sun. Early detection is essential for effective treatment, as skin cancer is highly manageable when identified in its initial stages. The conventional method for diagnosing skin cancer involves performing a biopsy, where a tissue sample is extracted and analyzed in a laboratory. Although this method is accurate, it is often time-consuming, invasive, and may cause discomfort to the patient. This project introduces an innovative approach that leverages Support Vector Machine (SVM) and image processing techniques to facilitate early detection of skin cancer. The process begins by capturing high-resolution dermoscopic images of the affected skin. These images undergo pre-processing steps, including noise reduction and image enhancement, to improve the quality and accuracy of subsequent analysis. Once pre-processing is complete, the enhanced images are segmented to isolate the region of interest, allowing for precise examination. The next critical step involves feature extraction using the Gray Level Co-occurrence Matrix (GLCM), a powerful statistical tool that analyzes textural patterns within the image. The extracted features, which include information about texture, contrast, and homogeneity, are then fed into the SVM classifier. The SVM, a robust machine learning algorithm known for its high classification accuracy, processes these features and classifies the image as either cancerous or non-cancerous. This streamlined approach not only reduces the time required for diagnosis but also minimizes the discomfort associated with traditional biopsy procedures, making  it a faster and less invasive solution for early detection of skin cancer disases..