Development of a Predictive Model for Kidney Stone Formation Using Deep Learning Techniques

This research investigates the development of a predictive model for kidney stone formation using deep learning techniques, specifically Convolutional Neural Networks (CNN), VGG 19, and Deep Belief Networks. Utilizing a dataset of 920 CT scans of images with varying sizes and locations, the model is used to predict the presence and location of kidney stones within the scan and the size of the kidney stones when present. The images undergo preprocessing, which includes standardizing the resolution, reducing noise, increasing contrast, and ensuring the spatial registry of all the images. Through this preprocessing, we are able to extract features from the images which, when fed through the models, can detect the subtle patterns that indicate the presence of the kidney stones. The dataset is split into two portions: one which the model is trained upon which contained 70% of the dataset and the other for validation, which encompassed 80% of the dataset. Various performance metrics were used to evaluate the performance of the models including accuracy, precision, recall, F1 score, and Area Under the Receiver Operating Characteristic (ROC) Curve AUC-ROC. The results have shown that the CNN model consistently led to better results than either the VGG 19 or DBN model across either the testing or validation data partitions; it yielded the highest levels in accuracy, precision, recall, and F1 score. The model which trained on 80% of the dataset performance yielded superior results compared to the model which trained on the 70% dataset, which further demonstrates the importance of size in training. The technique used in this research has the potential to revolutionize kidney stone detection and diagnoses, ensuring better planning for clinical decisions in patients with kidney stones.