Hybrid Support Vector Machine-Convolutional Neural Networks Multi-Classification Models for Detection of Kidney Stones

Abstract

The accurate and early detection of kidney stones is crucial for effective treatment and patient management. This study presents a hybrid machine learning approach combining Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) for the multi-classification of kidney stones. The proposed model leverages the feature extraction capabilities of CNNs with the robust classification performance of SVMs to improve diagnostic accuracy. The methodology is validated on a publicly available kidney stone dataset, and the experimental results demonstrate the superiority of the hybrid model over standalone CNN and SVM models. Different techniques, such as enhancing the contrast of the images, gray conversion to train with one channel, Gaussian filter to blur the noise of the images, data augmentation, and SMOTE to balance the dataset, using 5-fold cross-validation to prevent overfitting. Features that we extracted from CNN were optimized and classified using SVM, KNN, and RF. All the classifiers we incorporated showed a high overall classification accuracy of over 98%. Among these classifiers, the proposed Hybrid CNN-SVM model outperformed other models with a higher overall test accuracy of 98.49%. At the same time, CNN-KNN, CNN-RF, and CNN achieved an accuracy of 98.46%, 98.01%, and 97.62%, respectively. These classifiers show the effectiveness of hybrid models in reducing training time and improving classification accuracy compared to single models.