Proactive Pathological Assessment Via Machine Learning

The Kidney stones afflict millions worldwide, causing severe pain and potential complications such as obstruction and infection. Timely and accurate detection is crucial for effective treatment planning. This work presents a comprehensive, automated pipeline for kidney stone detection in grayscale medical images. The system integrates adaptive preprocessing (contrast enhancement and denoising), segmentation via adaptive thresholding and connected component analysis, feature extraction harnessing gray-level co-occurrence matrix (GLCM) and local binary patterns (LBP), outlier filtering using z-score metrics, and classification through convolutional neural networks (CNN), support vector machines (SVM), and random forests. An ensemble framework combines model outputs, yielding improved diagnostic performance over individual classifiers. We report accuracy, ROC-AUC, precision, recall, and F1-score on validation and test datasets. Experimental results demonstrate that the ensemble achieves an accuracy of 94%, surpassing single models, with potential for integration into clinical workflow.