Skin Melanoma Classification from Dermoscopy Images using ANU-Net Technique

Cells in any area of the body might develop cancer when they begin to grow uncontrollably. Other body regions may become affected by it. Skin cancer known as melanoma develops when melanocytes, or cells that create melanin, the pigment that gives skin its appearance of color, start to develop out of control. Melanoma is deadly because, if not caught early and addressed, it has a high propensity to spread to other regions of the body. Analyzing digital dermoscopy images, create a unique approach to categorizing melanocytic tumors as malignant or benign. Every single newly formed mole has a unique shape and colour compared to the pre-existing moles and given few more issues to classify the melanoma. To overcome all of these issues, this paper uses deep learning techniques. In this paper, a four-step system for classifying melanoma is described. The first stage is preprocessing, followed by the removal of hair from dermoscopic images using a Laplacian-based algorithm and then removing noise from the images using a Median filter. The second method is feature extraction from pre-processed images. Extracting features including texture, shape, and color using the Principal Component Analysis (PCA) technique. Thirdly, the LeNet-5 approach is utilized to locate the lesion location and segment the skin lesion. Fourth, the ANU-Net technique is used to categorize the lesion as cancerous (melanoma) or non-cancerous (nonmelanoma). Evaluated based on performance parameters such as precision, sensitivity, accuracy, and specificity. Results are compared to those of current systems and show higher accuracy.