Early Stage Skin Cancer Detection Using Image Processing

Early stage malignant cancer detection has gain utmost research importance in recent times. Carcinoma, which is currently acknowledged as the deadliest form of cancer in humans, appears in various types such as skin cancer, basal cell carcinoma, squamous cell carcinoma, and the relatively unexpected malignant melanoma. Timely identification of malignant melanoma is crucial for providing effective treatment. Computer vision systems have played a significant role in medical image classification, offering valuable contributions in this field. In this study, we propose a computer-aided method that relies on images to detect melanoma, a type of skin cancer. The system takes an image of a skin lesion as input and employs innovative image processing techniques to assess and determine the presence or absence of skin cancer. By analyzing factors such as texture, size, and segmentation, lesion image analysis tools examine a wide range of features associated with melanoma, including spatial characteristics, contours, color, diameter, and more. Based on derived feature parameters, the images are classified as either indicating melanoma or representing benign skin lesions.