Automated Melanoma Recognition A simple and effective code for Melanoma Recognition.
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Automated Melanoma Recognition Description

Malignant melanoma is nowadays one of the leading cancers among many white-skinned populations around the world. Change of recreational behavior together with the increase in ultraviolet radiation cause a dramatic increase in the number of melanomas diagnosed. We have developed a fast and reliable system that is capable to detect and classify skin lesions with high accuracy. We use color images of skin lesions, image processing techniques and AdaBoost classifier to distinguish melanoma from benign pigmented lesions. As the first step of the data set analysis, a preprocessing sequence is implemented to remove noise and undesired structures from the color image. Second, an automated segmentation approach localizes suspicious lesion regions by region growing after a preliminary step based on adaptive color segmentation. Then, we rely on quantitative image analysis to measure a series of candidate attributes hoped to contain enough information to differentiate melanomas from benign lesions. At last, the selected features are supplied to AdaBoost algorithm to build a strong classifier. Using Leave-one-out cross validation on Zagrouba’s image dataset (95 images of benign nevi and 25 images of malignant melanoma) we have obtained an excellent recognition rate of 86.10%. Give Automated Melanoma Recognition a try to fully assess its capabilities!

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