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Updated: Nov 8, 2025
Author Spotlight: Developing a Bedside Protocol for Kidney and Genitourinary Ultrasonography
Published on: June 21, 2024
Computer-aided diagnosis system for the classification of multi-class kidney abnormalities in the noisy ultrasound
1Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai-600127, India.
Insights
Early diagnosis of kidney diseases is crucial. A computer-aided diagnosis (CAD) system using deep learning for despeckling ultrasound images improves detection accuracy for kidney abnormalities.
Area of Science:
- Medical imaging analysis
- Artificial intelligence in healthcare
- Nephrology
Background:
- Chronic and polycystic kidney diseases are primary causes of kidney failure.
- Kidney diseases are often asymptomatic in early stages, necessitating early diagnosis.
- Cysts, stones, and tumors can impair kidney function.
Purpose of the Study:
- To develop a computer-aided diagnosis (CAD) system for detecting multi-class kidney abnormalities from ultrasound images.
- To enhance the performance of CAD systems by addressing speckle noise in ultrasound images.
- To improve early detection rates of kidney diseases to prevent kidney failure.
Main Methods:
- A CAD system utilizing a pre-trained ResNet-101 for feature extraction and a Support Vector Machine (SVM) classifier.
- Implementation of a despeckling module based on a deep residual learning network (RLN) for noise reduction in ultrasound images.
- Pre-processing ultrasound images with deep RLN to improve classification accuracy.
Main Results:
- The proposed CAD system achieved higher classification accuracy on noisy kidney ultrasound images compared to existing methods.
- The SVM classifier demonstrated superior performance over other tested classifiers (K-nearest neighbour, tree, discriminant, Naive Bayes, linear).
- The despeckling module significantly improved the CAD system's performance.
Conclusions:
- The developed CAD system accurately classifies noisy kidney ultrasound images, outperforming current state-of-the-art methods.
- The system demonstrates high selectivity and sensitivity scores.
- The CAD system with its pre-processing module can serve as a valuable real-time tool for diagnosing kidney abnormalities.
Background And Objective:
The primary causes of kidney failure are chronic and polycystic kidney diseases. Cyst, stone, and tumor development lead to chronic kidney diseases that commonly impair kidney functions. The kidney diseases are asymptomatic and do not show any significant symptoms at its initial stage. Therefore, diagnosing the kidney diseases at their earlier stage is required to prevent the loss of kidney function and kidney failure.
Methods:
This paper proposes a computer-aided diagnosis (CAD) system for detecting multi-class kidney abnormalities from ultrasound images. The presented CAD system uses a pre-trained ResNet-101 model for extracting the features and support vector machine (SVM) classifier for the classification purpose. Ultrasound images usually gets affected by speckle noise that degrades the image quality and performance of the CAD system. Hence, it is necessary to remove speckle noise from the ultrasound images. Therefore, a CAD based system is proposed with the despeckling module using a deep residual learning network (RLN) to reduce speckle noise. Pre-processing of ultrasound images using deep RLN helps to drastically improve the classification performance of the CAD system. The proposed CAD system achieved better prediction results when compared to the existing state-of-the-art methods.
Results:
To validate the proposed CAD system performance, the experiments have been carried out in the noisy kidney ultrasound images. The designed system framework achieved the maximum classification accuracy when compared to the existing approaches. The SVM classifier is selected for the CAD system based on performance comparison with various classifiers like K-nearest neighbour, tree, discriminant, Naive Bayes, and linear.
Conclusions:
The proposed CAD system outperforms in classifying the noisy kidney ultrasound images precisely as compared to the existing state-of-the-art methods. Further, the CAD system is evaluated in terms of selectivity and sensitivity scores. The presented CAD system with the pre-processing module would serve as a real-time supporting tool for diagnosing multi-class kidney abnormalities from the ultrasound images.
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