INTELLIGENT MELANOMA DETECTION BASED ON PIGMENT NETWORK

Abstract

Early detection of melanoma, the deadliest form of skin cancer, is critical for effective treatment. Detecting skin lesions accurately from dermoscopic images remains challenging, with the pigment network being a crucial indicator for melanoma detection. The accurate identification of pigment networks in dermoscopic images is difficult due to image noise and unwanted hair artifacts, which can obscure meaningful diagnostic features. To address these challenges, this thesis proposes novel image processing approaches for computer-aided pigment network detection. These methods aim to enhance image clarity by filtering out unwanted elements and converting images into binary formats, facilitating more precise analysis. Initially, preprocessing involves filtering dermoscopic images to remove noise and unwanted hair artifacts, enhancing image quality for more effective analysis. Subsequently, binary conversion and creation of binary masks further refine image clarity. The final step involves detecting pigment networks and calculating key features such as diameter and radius to assess melanoma presence. The proposed method achieves comparable accuracy and efficiency to existing algorithms. It demonstrates an average precision of 0.89 and average recall of 0.87, with an overall accuracy of 85.5%.