An Intelligent CT Image Analysis System for Automated Detection of Kidney Stones Using Deep Learning

Abstract

Kidney stone disease is a common ailment that needs to be diagnosed in due time and in an accurate manner to avoid extreme complications like obstruction of the kidney, infection and permanent damages to the kidney. Computed Tomography (CT) imaging is considered the gold standard of kidney stones detection as it has the high sensitivity and specificity. Manual interpretation of CT scans is difficult and time consuming however, it is likely to have inter-observer variation and extend a lot on the skills of radiologists. This paper seeks to solve these problems by coming up with an intelligent computerized tomography image analysis system to detect kidney stones automatically through deep learning methods. The paper uses a Convolutional Neural Network (CNN) that has been trained on a simulated set of labeled CT scans, which are positive and negative of kidney stones. Noise reduction, normalization, and data augmentation techniques were used to enhance the quality of the images and generalization of the model. The CNN model can automatically obtain hierarchical features of the images, which can be used to classify the images effectively without feature engineering. Accuracy, precision, recall, F1-score, specificity, and confusion matrix analysis of performance evaluation proved that the proposed system attained an average accuracy of 94.2, high sensitivity and low false negative rates. Comparative analysis indicated that CNN was better as compared to the traditional machine learning practices and the available models in the literature. The findings suggest that automated detection systems based on deep learning are capable of promoting efficiency in diagnostic, a decrease in workload of radiologists, and reliability in clinical decision-making. The paper offers a platform upon which future research can be undertaken on how best the incorporation of intelligent imaging systems can be integrated to real world medical practice especially in the healthcare settings, which are resource limited.