NORMA eResearch @NCI Library

Kidney Stone Detection using Deep Learning Methodologies

Irudayaraj, Alexander Albert (2022) Kidney Stone Detection using Deep Learning Methodologies. Masters thesis, Dublin, National College of Ireland.

[thumbnail of Master of Science]
PDF (Master of Science)
Download (1MB) | Preview
[thumbnail of Configuration manual]
PDF (Configuration manual)
Download (524kB) | Preview


Nephrolithiasis is a condition in which undesired sediments are deposited in the kidneys, interfering with the normal functioning of the urinary system and, in some cases, blocking urine flow, causing excruciating agony. The ability to detect kidney stones from medical imaging is thus critical for providing effective and timely medication. Deep Learning methods can be used to accurately perform object detection from images. The application of deep learning methods in kidney stone detection will help avert the percentage of errors that currently arise due to human error. In this paper four specific deep learning methods have been employed to detect whether kidney stones are present or not in CT scan images. The four algorithms used are VGG16, ResNet50V2, MobileNetV2, InceptionNetV3. A dataset containing 1799 CT scan images of kidneys was used for building these models to perform kidney stone detection. The classification performance of all four models were assessed using accuracy, precision, and recall metrics. InceptionNet neural network produced the best classification results in terms of accuracy, precision and recall. It produced an accuracy of 0.862, precision of 0.866 and recall of 0.8331. These measures are higher than the corresponding values for other three models by 11%, 10.5%, 2.9% respectively, hence this research confirms that among the four algorithms under consideration, InceptionNet is to be employed for automatic detection of kidney stones.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
R Medicine > R Medicine (General)
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
H Social Sciences > HM Sociology > Information Science > Communication > Medical Informatics
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 26 Jan 2023 16:35
Last Modified: 03 Mar 2023 11:18

Actions (login required)

View Item View Item