Published Paper Details:

It is a highly sensitive task in the treatment of the early stages and the provision of the treatment as the CT scan showed the presence of the tumors in the kidneys. Human hands can screen and categorize kidney tumors, therefore, subject to errors and wastage of time, computerized systems should be incorporated to make sure that the process has been improved to be effective and to achieve better results. The article recommends an efficient architecture of the deep learning i.e. the integration of the Transformer networks with the 3D Convolutional Neural Networks (CNNs) to increase the percentage of kidney tumor detection. The system rests upon the diversity of the available trained models i.e., DenseNet121, ResNet101, VGG19, InceptionV3, GoogleNet and MobileNet, to get features. In order to achieve classification, Fuzzy Neural Network (FNN), and Generalized Fuzzy Neural Network (GFNN) is implemented in a way that it could have been categorized in four classification niche; Cyst, Normal, Stone and tumor under which it would have been classified in four categories.