A Novel framework of Adaptive fuzzy-GLCM Segmentation and Fuzzy with Capsules Network (F-CapsNet) Classification

In this paper, offer a new framework for skin disease image recognition using deep learning techniques and local descriptor encoding approaches. For the purpose of detecting melanoma early, skin lesions must be accurately classified. In this research, an automatic image preprocessing approach is proposed for the removal of noise artefacts in photographs, including thin and thick hair objects, surgical ink markings, dark halo effects, and ebony frames. Due to hazy contrasts and distortions at the border margins, segmenting images are quite challenging. So, this research suggests a partitioning technique based on a fuzzy gray-level co-occurrence matrix (GLCM) that is both effective and adaptive. An alternative to convolutional neural networks (CNN) is proposed: the capsule-based network. An object's existence and the relationship between its functions are represented by a group of neurons (in logical units) that make up a vector called a capsule. While synthetic product neural networks use max-pooling layers to define capsule coupling between subsequent layers, capsule networks repeatedly utilise a dynamic routing technique to do so. Alternatively said, the routing-by-agreement approach offers learning between capsule layers. To assess the efficacy of the F-CapsNet technique, three widely used datasets—the ISIC 2017 Challenge, the 2019 Challenge, and the PH2 datasets—are employed. The suggested technique has an average accuracy of 99.16% for the ISBI 2017 test dataset and 99.45% accuracy for the ISBI 2019 test dataset. Additionally, the PH2 test dataset shows that the suggested approach has an average accuracy of 98.42%.