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Melanoma Detection Process

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Cancer of the skin is by far the most common of all cancers. Melanoma accounts for less than 2% of skin cancer cases but causes a large majority of skin cancer deaths. According to the American Cancer Society’s estimates for melanoma in the United States for2015: *About 73,870 new melanomas will be diagnosed (about 42,670 in men and 31,200 in women). *About 9,940 people are expected to die of melanoma (about 6,640 men and 3,300 women).The rates of melanoma have been rising for at least 30 years. Early diagnosis of malignant melanoma significantly reduces its morbidity ,mortality and its medication cost. To measure and detect sets of features from dermoscopic images, the computerized analysis of these images can be extremely useful and helpful …show more content…

This would enable supervised classification of melanocytic lesions. The melanoma detection process is composed of following steps that are the preprocessing, the segmentation, the feature extraction and feature selection ,thereby improving the classification …show more content…

It is used to correct defects illumination, eliminating noise and small spots and enhance the contours and contrast as much as possible without degrading the lesion.Preprocessing of the image is concerns with changing the colour image into gray scale image, removing the dark corners in the image and filtering to remove any artefacts in the image. Feature Extraction and Feature Selection The area I am concentrating is the feature extraction and Feature selection of clinical image. The feature extraction basically deals with the ABCD rule. This method is able to provide a more objective and reproducible diagnostic of skin cancers in addition to its speed of calculation.It is based on four parameters: A (Asymmetry) concerns the result of evaluation of lesions asymmetry, B (Border) estimates the character of lesions border, C (Color) identifies the number of colors present in the investigated lesion, and D (Diameter). Feature selection is a dimensionality reduction technique widely used and it allows elimination of (irrelevant/redundant) features, whilst retaining the underlying discriminatory information, feature selection implies less data

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