An Efficient and Robust Approach Using Inductive Transfer-Based Ensemble Deep Neural Networks for Kidney Stone Detection

3Citations
Citations of this article
17Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Chronic kidney disorder is a global health problem involving the repercussions of impaired kidney function and kidney failure. A kidney stone is a kidney scenario that impairs kidney function. Because this disease is usually asymptomatic, early and quick detection of kidney problems is essential to avoid significant consequences. This study presents an automated detection of Computed Tomography (CT) kidney stone images using an inductive transfer-based ensemble Deep Neural Network (DNN). Three datasets are created for feature extraction from kidney CT images using pre-trained DNN models. After assembling several pre-trained DNNs, such as DarkNet19, InceptionV3, and ResNet101, the ensemble deep feature vector is created using feature concatenation. The Iterative ReliefF feature selection method is used to choose the most informative ensemble deep feature vectors, which are then fed into the K Nearest Neighbor classifier tuned using a Bayesian optimizer with a 10-fold cross-validation approach to detect kidney stones. The proposed strategy achieves 99.8% and 96.7% accuracy using the quality and noisy image datasets, which are superior to other DNN-based and traditional image detection approaches. This proposed automated approach can help urologists confirm their physical inspection of kidney stones, reducing the possibility of human mistakes.

References Powered by Scopus

Ensemble deep learning: A review

762Citations
N/AReaders
Get full text

AI applications to medical images: From machine learning to deep learning

361Citations
N/AReaders
Get full text

Transfer learning for medical image classification: a literature review

340Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Optimized YOLOv5 Architecture for Superior Kidney Stone Detection in CT Scans

0Citations
N/AReaders
Get full text

A Hybrid Machine learning Techniques for Detection of Chronic Kidney Disease

0Citations
N/AReaders
Get full text

Kidney Disease Classification and Diagnosis: A Comprehensive Review of Current AI Techniques

0Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Chaki, J., & Ucar, A. (2024). An Efficient and Robust Approach Using Inductive Transfer-Based Ensemble Deep Neural Networks for Kidney Stone Detection. IEEE Access, 12, 32894–32910. https://doi.org/10.1109/ACCESS.2024.3370672

Readers over time

‘2405101520

Readers' Seniority

Tooltip

Professor / Associate Prof. 2

100%

Readers' Discipline

Tooltip

Engineering 2

50%

Agricultural and Biological Sciences 1

25%

Computer Science 1

25%

Article Metrics

Tooltip
Mentions
News Mentions: 1

Save time finding and organizing research with Mendeley

Sign up for free
0