Abstract
Kidney stone disease is a common syndrome and a recurring one, where it bears a 50% chance of being manifested again within ten years and may lead to serious complications like ureteral obstruction and unbearable pain. If timely intervention is considered of paramount importance for a timely intervention, early and accurate detection using computed tomography (CT) scans is also critical to this process. Existing diagnostic systems are being challenged by factors like noise in images, low contrast, and class imbalance, and these might hamper the performance of existing systems. This work focuses on developing an optimized framework of deep learning for the detection of kidney stones in CT images to deal with these drawbacks. The overall proposed approach consists of a preprocessing scheme to normalize the data using Wang-Mendel (WM) de-noising and enhancing contrast globally, followed by data augmentation with the use of SdSmote to overcome an imbalance in the classes. The pre-processed images will be fed into a modified Bidirectional Recurrent Neural Network (BRNN), which will undergo optimization of the weights and biases using a newly implemented Bald Eagle Search (BES) algorithm, with quasi-oppositional learning and chaotic initialization introduced to increase convergence and global search capability. The proposed method is applied to the public CT Kidney Dataset, compared with state-of-the-art techniques like ensemble learning, Exemplar Darknet19, DE/SVM, and Decision Tree solutions. The proposed means attained better performance, showing 96.96% accuracy, 95.62% sensitivity, 91.67% specificity, 94.38% precision, 94.99% F1-score, and 91.61% in the Jaccard Index, thereby confirming the effectiveness and robustness of the proposed model in clinical decision-making concerning kidney stone diagnosis.