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Related Concept Videos

Chronic Kidney Disease I: Introduction01:25

Chronic Kidney Disease I: Introduction

Chronic Kidney Disease (CKD) arises when the kidneys progressively lose their ability to function, ultimately leading to end-stage renal disease. At this advanced stage, the kidneys can no longer filter waste or maintain essential body functions, requiring renal replacement therapy (RRT) through dialysis or a kidney transplant for survival.Early-stage chronic kidney disease and detection challengesIn CKD's early stages, symptoms often remain absent because healthy nephrons compensate for...
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Chronic kidney disease (CKD) requires collaborative and comprehensive management. CKD progresses through stages and can lead to end-stage kidney disease (ESKD) if untreated. Interprofessional collaboration and patient education are crucial, enabling patients to manage their health and improve their quality of life.Diagnostic approach for chronic kidney diseaseThe diagnosis of CKD primarily focuses on the glomerular filtration rate (GFR), which assesses kidney function by measuring how well...
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Chronic Kidney Disease II: Clinical Manifestations

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Chronic renal disease classification using the AL-5-ENeT-B4 model from CT images.

Ram Kishun Mahto1, Pushpendra Kumar1, Subhash Chandra Yadav1

  • 1Department of Computer Science and Engineering, Central University of Jharkhand, Ranchi, India.

Journal of Medical Engineering & Technology
|July 14, 2026
PubMed
Summary

This study introduces AL-5-ENeT-B4, an AI model for classifying Chronic Renal Disease (CRD) from CT scans. It achieves high accuracy in identifying renal abnormalities, aiding early diagnosis and treatment.

Keywords:
Conventional diagnosticEfficientNetclassificationcomputed tomographydeep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Nephrology

Background:

  • Chronic Renal Disease (CRD) presents a growing global health challenge, often linked to diabetes and hypertension.
  • Accurate, early detection of renal abnormalities in CT images is crucial but challenging due to manual interpretation limitations and conventional AI scalability issues.
  • Automated classification of medical images for conditions like CRD requires robust and efficient deep learning models.

Purpose of the Study:

  • To develop and evaluate an advanced deep learning model, AL-5-ENeT-B4, for automated classification of Chronic Renal Disease (CRD) from CT images.
  • To enhance an EfficientNet-B4 architecture with additional layers for improved accuracy in multiclass renal CT scan classification.
  • To compare the performance of the proposed model against established deep learning architectures.

Main Methods:

  • A modified EfficientNet-B4 architecture (AL-5-ENeT-B4) was developed, incorporating five additional layers.
  • The framework included image preprocessing, data augmentation, and stratified 5-fold cross-validation.
  • Deep learning with pre-trained features was employed to classify renal CT scans into four categories: cyst, normal, stone, or tumor.

Main Results:

  • The AL-5-ENeT-B4 model achieved high performance metrics, including an average 5-fold cross-validation accuracy of 99.18%, precision of 99.18%, recall of 99.19%, and F1-score of 99.18%.
  • Grad-CAM visualization was utilized for enhanced interpretability, highlighting clinically significant regions in the CT scans.
  • The proposed model significantly outperformed ResNet50, VGG16, DenseNet121, and ViT-B/16, evidenced by average MCC (0.9912) and Cohen's Kappa (0.9912) values (p < 0.001).

Conclusions:

  • The AL-5-ENeT-B4 model demonstrates superior performance and interpretability for automated CRD classification from CT images compared to existing models.
  • This AI-driven approach offers a promising tool for early and accurate detection of renal abnormalities, potentially improving patient outcomes.
  • The study highlights the effectiveness of modified EfficientNet architectures in complex medical image analysis tasks.