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Skin Cancer Detection Using Image Processing
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In todays world, Skin cancer is a major cause that leads to death amongst humans. Abnormal growth of skin cells is known as skin cancer. Mostly it develops on places where body is exposed to sunlight, but it also can occur anywhere on the body. Usually, Skin cancers are curable when treated on their early stages. So it is vital to detect skin cancer as early as possible to save the life of the patient before the cancer reaches terminal stage where treatment is very tough and ineffective. By using latest technologies, it is possible to detect skin cancer at the initial stage itself. Traditional methodology of detecting skin cancer involves Biopsy method. The patient has to undergo skin cell removing for lab tests which is effective but causes pain to the patient and also a time-consuming method for detection of cancer detection. Our


technology detects cancer present in our human skin by the utilization of an Artificial Neural Network. This methodology utilizes Image enhancement techniques for better results than the existing industrial methods which are using MATLAB. The affected skin area image will be to go through dermoscopy image prepossessing techniques for noise elimination and then followed by image enhancement techniques. The enhanced image will be then subjected to go segmentation by Thresholding. Where some features are unique for skin cancer and those features makes it possible to identify whether the processed image is cancerous or not. Those features were extracted by one of our feature extraction model called 2D WTM. These features are fed to the neural network as input. BPN is utilized for grouping purpose. Where it groups the provided information group into Melanoma or Nevus.

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