Accurate detection of kidney stones using attention-based graph models

Abstract Kidney stones, formed by the accumulation of minerals and salts in the kidneys, can lead to severe pain and significant complications. Timely and accurate detection of these stones, particularly in medical imaging like CT scans, is crucial for effective treatment. This study introduces a novel method for detecting and classifying kidney stones in CT images, addressing the challenges posed by the variety of stone shapes and sizes, image noise, and the difficulty of identifying small or atypical stones. The proposed method utilizes a graph attention network to enhance detection accuracy and minimize identification errors. Initially, keypoints are extracted from the images using the ORB algorithm. These keypoints are considered as nodes in a graph, and using an appropriate threshold, the nodes are connected to each other to form the graph. The relationships between them are then defined through an adjacency matrix. Subsequently, for each node, a feature vector is obtained by using the pixel values within a 5 × 5 window centered on the corresponding node, and a feature matrix is constructed for the entire graph. The graph is then transformed into an embedded space, where features are updated through the graph attention network. This attention mechanism allows the model to discern the significance of each node and its connections, effectively extracting structural information. The updated embedded data is subsequently fed into a deep neural network for training and classification. Evaluation of the model’s performance is conducted using a dataset of coronal CT scan images. The results indicate that the proposed model achieves an impressive accuracy of 99.14% in detecting kidney stones, significantly outperforming existing methods, particularly in identifying small and atypical stones. This study highlights the effectiveness of integrating graphical features with attention mechanisms for enhanced medical image analysis, aiding in the early and accurate diagnosis of kidney stones.