International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Automated Segmentation and Classification of Chronic Kidney Disorders Using Renal Ultrasound Images |
|---|---|
| ABSTRACT | Chronic kidney disease (CKD) represents one of the most prevalent and pro-gressively debilitating non-communicable disorders globally, with early and ac-curate diagnosis being paramount to preventing irreversible renal failure. Re-nal ultrasound imaging, owing to its non-invasive nature, cost-effectiveness, and widespread clinical availability, is the first-line modality for evaluating kidney morphology. However, manual interpretation of ultrasound images is inherently subjective, time-intensive, and heavily dependent on operator expertise, resulting in significant inter-observer variability. To address these limitations, this paper presents an automated, end-to-end deep learning framework for the segmentation and multi-class classification of chronic kidney disorders—including renal cysts, nephrolithiasis (kidney stones), and hydronephrosis—directly from B-mode renal ultrasound images. The proposed system integrates a hybrid architecture com-bining an Attention U-Net for precise anatomical boundary segmentation with a ResNet-50-based classification backbone augmented by spatial attention gates. The segmentation module delineates the renal parenchyma and lesion regions of interest, while the classification module categorizes each detected region into one of five diagnostic classes: normal, cystic disease, nephrolithiasis, hydronephrosis, and chronic parenchymal disease. Trained and evaluated on a curated dataset of 4,200 annotated renal ultrasound images acquired from three clinical institutions, the system achieved a mean Dice Similarity Coefficient (DSC) of 0.912 for seg-mentation and a classification accuracy of 94.6%, with a macro-average F1-score of 0.931. These results significantly outperform existing U-Net and VGG-16 base-lines, demonstrating the clinical viability of the proposed framework as a reliable computer-aided diagnosis (CAD) tool for CKD assessment. |
| AUTHOR | SIMRAN UPASANI, PROF. DR. B.D. PHULPAGAR M. Tech Student, Department of Computer Engineering, P.E.S College of Engineering, Affiliated to SPPU, Pune, India Guide, Department of Computer Engineering, P.E.S College of Engineering, Affiliated to SPPU, Pune, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406052 |
| pdf/52_Automated Segmentation and Classification of Chronic Kidney Disorders Using Renal Ultrasound Images.pdf | |
| KEYWORDS | |
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