Multi-feature Response Attention Network for kidney stone detection and measurement of stone size in ultrasound images
- 1Department of Mathematics and Computer Science, Modern College of Business and Science, Bowshar, Muscat, Sultanate of Oman
- 2Intelligent Computing and Business Systems (ICBS), St. Joseph Engineering College, Mangalore, India
- 3Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
Abstract
Kidney stones are hard deposits formed in the urinary tract due to excess waste and low fluid intake, causing severe abdominal pain. Ultrasound-based kidney stone detection involves identifying and locating stones in the kidneys using high-frequency sound waves to visualize renal structures. Measuring stone size is crucial for early treatment and preventing infection. However, detection is challenging because ultrasound images suffer from noise, lower contrast, and variable quality. To overcome these limitations, the Multi-feature Response Attention Network (MFRANet) is proposed to detect kidney stones. Initially, the input image is allowed for image denoising and edge preservation using Windowed Variation Kernel Wiener Filter (WV-KWF). Further, the kidney region is segmented by the Fuzzy Clustering algorithm with Variable Multi-pixel Fitting spatial information (FCM-VMF). Moreover, descriptor computation is processed by rotated Binary Robust Independent Elementary Feature (rBRIEF) with Discrete Cosine Transform (DCT). Afterwards, the kidney stone is detected using MFRANet, wherein MFRANet is designed by combining Multi-feature Multi-Attention Network (MFMANet), along with Group Response Attention Network (GRA-Net). Next, the detected stone is segmented using Optimized DA-TransUNet (O-DAT), and lastly, the kidney stone size is measured. Performance of the proposed MFRANet is assessed with Accuracy, F1-score, Precision, and True Positive Rate (TPR) which achieved superior values of 96.64%, 96.65%, 97.44%, 95.88% Response Attention Network, Multi-feature Multi-Attention Network, Optimized DA-TransUNet.
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BibTeX
@article{padmanabha2026multifeature,
title = {{Multi-feature Response Attention Network for kidney stone detection and measurement of stone size in ultrasound images}},
author = {Vijaya Padmanabha and Mohamed Sirajudeen Yoosuf and Anand Mahendran and Joseph Mani and Anisha Prima Rodrigues and Gururaj Bijur and Hothefa Jassim},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0359214},
url = {https://doi.org/10.1371/journal.pone.0359214},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Multi-feature Response Attention Network for kidney stone detection and measurement of stone size in ultrasound images
AU - Vijaya Padmanabha
AU - Mohamed Sirajudeen Yoosuf
AU - Anand Mahendran
AU - Joseph Mani
AU - Anisha Prima Rodrigues
AU - Gururaj Bijur
AU - Hothefa Jassim
PY - 2026
DA - 2026/10/15
JO - PLOS One
DO - 10.1371/journal.pone.0359214
UR - https://doi.org/10.1371/journal.pone.0359214
AB - Kidney stones are hard deposits formed in the urinary tract due to excess waste and low fluid intake, causing severe abdominal pain. Ultrasound-based kidney stone detection involves identifying and locating stones in the kidneys using high-frequency sound waves to visualize renal structures. Measuring stone size is crucial for early treatment and preventing infection. However, detection is challenging because ultrasound images suffer from noise, lower contrast, and variable quality. To overcome these limitations, the Multi-feature Response Attention Network (MFRANet) is proposed to detect kidney stones. Initially, the input image is allowed for image denoising and edge preservation using Windowed Variation Kernel Wiener Filter (WV-KWF). Further, the kidney region is segmented by the Fuzzy Clustering algorithm with Variable Multi-pixel Fitting spatial information (FCM-VMF). Moreover, descriptor computation is processed by rotated Binary Robust Independent Elementary Feature (rBRIEF) with Discrete Cosine Transform (DCT). Afterwards, the kidney stone is detected using MFRANet, wherein MFRANet is designed by combining Multi-feature Multi-Attention Network (MFMANet), along with Group Response Attention Network (GRA-Net). Next, the detected stone is segmented using Optimized DA-TransUNet (O-DAT), and lastly, the kidney stone size is measured. Performance of the proposed MFRANet is assessed with Accuracy, F1-score, Precision, and True Positive Rate (TPR) which achieved superior values of 96.64%, 96.65%, 97.44%, 95.88% Response Attention Network, Multi-feature Multi-Attention Network, Optimized DA-TransUNet.
SN - 1932-6203
ER -