Computer aided segmentation approach for Melanoma skin cancer detection

Abstract

Skin cancer is the most common type of cancer in the world and nowadays, this incidence is increasing rapidly. In recent years, there has been a fairly rapid increment in melanoma skin cancer patients. Melanoma, this the deadliest form of skin cancer, must be diagnosed earlier as soon as possible for effective treatment. To diagnose melanoma earlier, skin lesion should be segmented accurately. However, the segmentation of the melanoma skin cancer lesion in traditional approach is a challenging task due to the number of false positives is large and time consuming in prediction. Hence, the development of automated computer vision system becoming as an essential tool to segment the skin lesion from given photograph of patient’s cancer affected area and to overcome those difficulties, which were found in the earlier methods. This work was done through image processing techniques. Some of these techniques are widely used in similar applications, as is the case of the canny edge detection for finding the lesion boundary. Other techniques are watershed segmentation for segmenting the lesion from skin, multilevel thresholding for merging the lesion, and active contour for increasing the accuracy. Though the personnel in the medical field had introduced new methodologies to improve the accuracy by addressing the challenges and mainly focusing on the accuracy, the approach in this study achieved 97.54% sensitivity, 97.69% specificity, and 97.56% accuracy.