Early-Stage Edge Detection of Skin Cancer through Image Processing Methods

In today’s modern world, skin cancer is the most common cause of death among humans. Skin cancer is an abnormal growth of skin cells that most often develops on the body when exposed to sunlight, but can occur anywhere on the body. Most skin cancers are curable when detected at an early stage. Therefore, early and rapid detection of skin cancer can save a patient’s life. With the new technology, early detection of skin cancer is now possible at the earliest stages. The formal method for diagnosing skin cancer is a biopsy. It is performed by removing skin cells, after which the sample is subjected to various laboratory tests. It is a painful and time-consuming process. We propose a skin cancer detection system that uses edge detection methods (EDMs) to enable early detection. It is more advantageous to patients. The diagnostic methodology employs image processing techniques to enhance early-stage detection and to guide treatment. Capture the high-resolution image, then it is subjected to different EDMs such as Canny, Sobel, and Prewitt to make an updated image. Performance is assessed using PSNR, SSIM, and MSE. Each numerical value is accompanied by a thorough explanation of the input’s relation to the EDMs. The results of the methods will be based on edge-detection measures. This study is innovative because it uses multiple EDMs on high-resolution skin images to identify early-stage lesions without invasive procedures, offering a faster and less painful alternative to standard biopsy. By quantitatively assessing image quality, the method increases diagnostic efficiency and facilitates the more rapid and precise detection of skin cancer.