Abstract
Internet of Medical Things (IoMT) includes interconnected sensors, wearable devices, medical devices, and clinical systems. At the same time, skin cancer is a commonly available type of cancer that exists all over the globe. This study projects a new segmentation based classification model for skin lesion diagnosis by combining a GrabCut algorithm and Adaptive Neuro-Fuzzy classifier (ANFC) model. The proposed method involves four main steps: preprocessing, segmentation, feature extraction, and classification. Initially, the preprocessing step is carried out using a Top hat filter and inpainting technique. Then, the Grabcut algorithm is used to segment the preprocessed images. Next, the feature extraction process takes place by the use of a deep learning based Inception model. Finally, an adaptive neuro-fuzzy classifier (ANFC) system gets executed to classify the dermoscopic images into different classes. The proposed model is simulated using a benchmark International Skin Imaging Collaboration (ISIC) dataset and the results are examined interms of accuracy, sensitivity and specificity. The proposed model exhibits better identification and classification of skin cancer. For examining the effective outcome of the projected technique, an extensive comparison of the presented method with earlier models takes place. The experimental values indicated that the proposed method has offered a maximum sensitivity of 93.40%, specificity of 98.70% and accuracy of 97.91%.