Kidney Stone Detection Using Medical Imagingand Computational Intelligence - A Review

A Comprehensive Review on Kidney Stone Detection using Machine Learning and Deep Learning Techniques

Kidney stone disease is a common urological disease, which impacts a significant proportion of the population across the globe. It should be diagnosed early and properly because in the cases that are not properly dealt with, the complications may include obstruction of urine and permanent damage to the kidneys. Computed Tomography (CT) imaging is usually the method of choice among the existing diagnostic techniques because it is highly sensitive and can clearly display the arrangement of the stones. Nevertheless, the process of manual interpretation of medical images can be time-consuming and can be subject to variation, since the medical diagnosis can be determined by the experience of the radiologist. As computational methods developed, there has been an increase in interest in using Machine Learning (ML) and Deep Learning (DL) methods to automate the process of kidney stone detection. These techniques have demonstrated a possibility of enhancing uniformity and diagnostic capability. The review provides a summary of current approaches to kidney stones detection, including classic image processing algorithms, classical machine learning systems, and more modern deep learning models like Convolutional Neural Networks (CNNs) and object detection models like YOLO. Moreover, the paper addresses popular datasets, metrics of evaluation, and practical issues, such as limited data, the challenge of identifying stones of a small size, and the problem of model generalization in various clinical scenarios. Other recent directions, including hybrid modeling and explainable AI are also discussed, which could enhance the transparency and clinical adoption of automated systems. On the whole, this survey will equip a systematic knowledge of the existing trends and also pinpoint areas that need to be researched more in the context of automated kidney stone detection.

A deep learning-based study on automated CT diagnosis of kidney stones, hydronephrosis and pyonephrosis.

Laplacian sharpening achieves the best performance within the unified MSF-TEA Net framework, with a test accuracy of 94.40% ± 1.13% and an AUC of 99.30%, outperforming the other enhancement strategies.

Explainable deep learning models for kidney stone classification using axial CT scans

Deep Learning-Based Detection of Renal Abnormalities from CT Images: A Study on a Bangladeshi Cohort

This study presents an AI-driven computed tomography (CT) diagnostic system for the automated detection and classification of renal abnormalities in the Bangladeshi population. Renal abnormalities such as kidney cysts, stones, and tumors require timely and accurate diagnosis to reduce complications and improve treatment outcomes. In Bangladesh, the increasing burden of kidney-related diseases has created a strong need for efficient and intelligent diagnostic support systems. To assess the effectiveness of deep learning in multiclass renal abnormality diagnosis, three advanced convolutional neural network architectures were implemented and compared: Xception, VGG16, and ResNet152V2. The experimental results demonstrated outstanding classification performance across all models, with Xception achieving the highest accuracy of 99.84%, followed by ResNet152V2 at 99.76%, and VGG16 at 99.40%. Among the evaluated approaches, Xception showed the best overall performance, indicating its strong capability for reliable renal abnormality classification from CT images. The proposed system has significant potential to assist radiologists and healthcare professionals by providing fast, accurate, and automated diagnostic support. This work highlights the promise of artificial intelligence in medical imaging and contributes to the advancement of intelligent diagnostic solutions for kidney disease detection in Bangladesh.

Chronic renal disease classification using the AL-5-ENeT-B4 model from CT images.

Chronic Renal Disease (CRD) is an increasing global health burden, largely driven by the rising prevalence of diabetes and hypertension. Early and accurate identification of renal abnormalities from computed tomography (CT) images is clinically important, yet manual interpretation is time-consuming and conventional machine learning methods often have limited scalability for complex multiclass medical image classification. This study proposes AL-5-ENeT-B4, a modified EfficientNet-B4 architecture enhanc d with five additional layers for automated CRD classification from CT images. The proposed framework is integrated an image preprocessing, data augmentation, stratified 5-fold cross-validation, and deep learning of pre-trained features to classify renal CT scans into one of the four categories: cyst, normal, stone, or tumour. The results of this model were evaluated using 5-fold cross-validation. The proposed model achieved an average K-fold accuracy (99.18%), precision (99.18%), and recall score (99.19%), with an F1-score (99.18%). Furthermore, Grad-CAM visualisation used to provide better interpretability by highlights those renal areas that had clinical significance in relation to the model's decisions. The proposed AL-5-ENeT-B4 model is compared with ResNet50, VGG16, DenseNet121, and ViT-B/16 and demonstrated significantly improved performance achieving an average MCC of 0.9912 and Cohen's Kappa of 0.9912(p < 0.001).

Explainable Kidney Stone Detection and Localization in CT with U-Net, EfficientNet, and Multi-Method XAI

Kidney stone disease affects approximately 10% of the global population, underscoring the clinical importance of accurate CT-based stone classification and precise localization. This paper presents a leakage-controlled deep learning framework that delivers both pixel-level localization and transparent image-level prediction through two complementary model pipelines that are trained and evaluated independently. In the first pipeline, a U-Net is trained for grayscale-to-color reconstruction and stone localization; a binary stone mask is then obtained via post-processing to visually delineate suspected regions. In the second pipeline, an EfficientNet-B0 model is trained for binary classification (Normal vs. Stone) using patientdisjoint train/validation/test splits to mitigate data leakage and better reflect realistic deployment, and is benchmarked against standard Convolutional Neural Network (CNN) baselines. We further incorporate explainable AI to validate model reasoning: Gradient-weighted Class Activation Mapping (Grad-CAM) highlights discriminative regions, Local Interpretable Modelagnostic Explanations (LIME) provides superpixel-based local evidence, and SHapley Additive exPlanations (SHAP) assigns pixel-wise contribution scores. Across experiments, grayscale representations substantially outperformed color inputs, and augmentation improved generalization. Using two state-of-theart datasets, the proposed classifier achieved 99.76% and 99.65% test accuracy, respectively. For segmentation/localization, the U-Net component achieved Dice scores of 0.91-0.93 on the first dataset and 0.99 on the second dataset. Qualitative explainability results consistently focused on stone-relevant kidney regions, supporting interpretability and clinical plausibility of predictions. These findings indicate that combining robust training practices with multi-method explainability can yield highly accurate and clinically interpretable kidney stone analysis from CT images.