Automatic Segmentation and Extraction of Skin Lesion in Dermoscopic images using Image Processing

Dermoscopy, an epiluminescence light microscope that magnifies lesions and enables investigation down to the dermo-epidermal interface, is a non-invasive method that doctors may use to help with the diagnosis of melanoma. The research on melanoma skin cancers and the many subtypes of each is included in our paper. The four steps of the approach are preprocessing, segmentation, extraction of features, and classification. The artifact-filled image is preprocessed during the preprocessing stage using noise filtering and contrast sharpening. The processed image (Fuzzy C-Means method) immediately initiates a segmentation method. K-means clustering outperforms fuzzy C-Means in terms of outcomes for overlapping data sets. Gabor then does feature extraction. The Gabor filter and Glcm perform feature extraction in the next stage. By computing the pair of pixels with defined values and appearing in a given spatial image in an image, Glcm is used to characterise the image. The Gabor filter determines if a picture contains any certain frequency content. Last but not least, for classification, the bulk of the algorithms described in the literature rely on a feature extraction stage that is followed by a classifier that takes the retrieved features as inputs. However, Support Vector Machine is utilised for classification in our method. SVM produces spaces with many dimensions. The picture is categorised according to the type of melanoma malignancies in the classification output.