Deep Learning Technique for Dermoscopy Image Processing Model for Early Melanoma Detection

The impact of melano ma has grown over the past three decades, making early detection more important for reducing mortality rates. In light of this, a dermoscopy image of a tumor and/or skin pigmentation coupled with an automatic, trustworthy system capable of detecting the presence of melanoma can be a powerful tool in the field of medical diagnostics. When it comes to cancer-related mortality, skin cancer consistently places near the top world wide. Fewer lives will be lost to skin cancer if it can be diagnosed and treated early. Automatic detection of skin lesions using dermoscopic photographs is still a tough and complicated task, despite the increased awareness of me lanoma and the medical community's efforts to develop a therapy. Using computers to evaluate images of skin lesions is crucial for early detection of skin cancer. Skin cancer has surpassed other forms of cancer as the most common cause of cancer death in people in recent years. Accurate melanoma diagnosis requires combining dermatological knowledge with computer vision techniques. To aid in the early diagnosis of melanoma, novel diagnostic techniques are urgently needed. Recent technological developments have enhanced a variety of computer-aided diagnostics for early detection of melanoma skin cancer. Four processes pre-processing, segmentation, feature extraction, and classification are required for accurate melanoma diagnosis. This research presents a brief survey on melanoma image segmentation, feature extraction and selection and classification models. This research helps numerous research scholars, academicians to understand the working of available models and their limitations and to design innovative and accurate solutions for accurate melanoma detection.