An Implementation of Adaptive Multi-CNN Feature Fusion Model With Attention Mechanism With Improved Heuristic Algorithm for Kidney Stone Detection

Nowadays, most people have been admitted to emergencies with severe pain caused by kidney stones worldwide. In this case, diverse imaging approaches are aided in the detection process of stones in the kidney. Moreover, the specialist acquires better diagnosis and interpretation of this image. Here, computer-aided techniques are considered the practical techniques, which it is used as the auxiliary tool for the process of diagnosis. Most urologists have failed to train the type of kidney stone identification effectively and it is operator-dependent. Concerning the surgical operation, there is a requirement for accurate as well as adequate detection of stone position in the kidney. Thus, it has made the detection process even more difficult. To overcome the challenging issues, an effective detection model for kidney stones using classifiers. Initially, the input images are collected from the standard resources. Further, the input images are subjected to the adaptive multi-convolutional neural network with attention mechanism (AMC-AM) feature fusion model, in which, the pertinent features are extracted from the three networks: Visual Geometry Group16 (VGG16), Residual Network (ResNet), and Inception net. Thus, the three distinct features are obtained for the feature fusion procedure. Finally, the resultant features are subjected as input to the final layer of CNN. In the proposed network, the model is integrated with the attention mechanism and also the parameter tuning is done by proposing the modified social distance of coronavirus mask protection algorithm (MSD-CMPA). Therefore, the performance is examined using different metrics and compared with other baseline models. Hence, the proposed model overwhelms the outstanding results in detecting the kidney stones that aid the individual in getting rid of kidney disorders.