Vıt-skınnet: Segmentasyon mekanizması ile etkili cilt kanserinin tespiti için yeni bir vizyon transformatör tabanlı ShufflenetV3 modeli

Melanoma, a deadly type of skin cancer, claims hundreds of lives every year. Skin cancer typically affects areas of skin that are exposed to sunlight on a frequent basis, such as the "legs, face, arms, and neck". By visually examining lesions with pigment on the skin, melanoma can be identified early and treated with a straightforward method of removal of the malignant cells. However, the examination of the skin by the naked eye alone has a restricted and inconsistent accuracy owing to the scarcity of dermatologists. This also leads the patients to undergo multiple biopsies and thus hampers the course of treatment. The automatic identification of lesions from the dermoscopy images involves several obstacles due to the intricate lesion characteristics and as a result of the detection backdrop. There is a dearth of research on major intra-class variations and inter-class similarities of lesion characteristics, and the prior solutions primarily concentrate on employing larger and more complicated systems for detecting the presence of skin cancer with much-enhanced detection accuracy. In order to address various issues with the traditional methods, it is therefore increasingly crucial to create a successful structure for the identification of skin cancer through the use of deep learning approaches. The implemented skin cancer detection and classification model has three crucial procedures to perform. The collection of dermoscopy images, the segmentation process for segmenting the images, and the detection phase are the three main stages of the implemented skin cancer detection system that is put into practice. First, the benchmark images are utilized to provide the images that are needed for the tests. Once the images are gathered, then the segmentation phase is executed. The segmentation step receives the gathered images as its input. Then, the Residual DenseUNet++ (ResDenseUNet++) is used to carry out an effective segmentation. At this point, the resultant segmented image is provided by the developed ResDenseUNet++, which is then considered for the later stage's inputs. Additionally, the segmented image is then given to the feature extraction process by which the gradient filter image and the texture pattern image are obtained. Additionally, the obtained image results are then inputted for the phase of skin cancer detection. During the detection phase, the newly developed ShufflenetV3 based on Vision Transformer (ViT-ShufflenetV3) is implemented and used to effectively recognize skin cancer. In many experimental validations, the recommended skin cancer identification system has ensured a more precise classification outcome regarding skin cancer than other traditional models.