Diagnosing of Dermoscopic Images using Machine Learning approaches for Melanoma Detection

In recent years, the rate of skin diseases is increasing worldwide, skin cancer is defined as the rapid growth of skin cells due to DNA damage which cannot be repaired. It can be harmful and can lead to death if not diagnosed at early stages. The rapid growth of technology, makes it possible to detect different skin diseases at early stages. The impact of rapid technological change on sustainable development in the areas of image processing and machine learning gives an ability to detect early, which increases the probability of survival in the cancer patients. This research primarily focuses on segmentation and classification of the skin lesions from the MRI scan images. Segmentation is carried out in three stages which are pre-processing, segmentation and postprocessing. In the second section, classification is performed using feature extractor and different classifiers. The features are firstly extracted using color, shape, texture component of the skin lesion, and then concatenate the result of all feature distractors. Finally, assign the result of feature distractor to a different classifier to compare most and good classifiers used in this article. The result analysis of the proposed system shows the Naive Bayes, Logistic Regression, and Support Vector Machine classifier provide a better accuracy up to 92%,92%,89.6% respectively among other classifiers.