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Related Concept Videos

Chronic Kidney Disease I: Introduction01:25

Chronic Kidney Disease I: Introduction

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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 II: Clinical Manifestations01:24

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Chronic Kidney Disease (CKD) progressively impairs multiple body systems due to the accumulation of uremic toxins, which disrupt cellular functions across various organs.Neurologic symptomsNeurologic symptoms often arise early in CKD, as uremic toxin buildup drives changes in cognitive and motor functions. Patients frequently experience fatigue, headache, confusion, difficulty concentrating, and, in severe cases, seizures. Peripheral neuropathy commonly manifests as burning sensations in the...
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Chronic Kidney Disease III: Interprofessional Care01:28

Chronic Kidney Disease III: Interprofessional Care

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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 IV: Nursing Management01:18

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Kidney Structure01:45

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The kidneys are two large bean-shaped organs located in the upper abdomen. They filter the blood several times a day to remove toxins and rebalance water and electrolytes of the circulatory system via the renal veins. The kidneys receive blood directly from the heart via the renal arteries. These arteries enter the kidney at the hilum, the concave surface of the bean, where they branch and divide into smaller vessels and capillaries.
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Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

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Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
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A two-stage deep learning framework for kidney disease detection using modified specular-free imaging and

Noha A El-Hag1, Walid El-Shafai2,3, Hayam A Abd El-Hameed4

  • 1The Higher Institute of Commercial Sciences, Al mahalla Al kubra, Algarbia, 31951, Egypt.

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|February 11, 2026
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Summary

A new two-stage diagnostic model significantly improves kidney disease detection. Utilizing a Modified Specular-Free technique and EfficientNet-B2, it achieves 98.27% accuracy in identifying renal pathologies.

Keywords:
Deep learningDiagnostic accuracyEfficientNet-B2Image enhancementKidney disease detectionMedical imagingModified specular-free imagingPathology classification

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Nephrology

Background:

  • Kidney diseases pose a growing public health challenge with increasing incidence.
  • Accurate and early diagnosis of kidney pathologies is crucial for effective patient management.
  • Existing diagnostic methods may face limitations in image quality and classification accuracy.

Purpose of the Study:

  • To develop and validate a novel two-stage diagnostic model for enhanced detection of kidney diseases.
  • To improve the visual quality of renal images using an advanced enhancement technique.
  • To achieve high accuracy in classifying various renal pathologies.

Main Methods:

  • Implementation of a Modified Specular-Free (MSF) technique for adaptive renal image enhancement.
  • Classification of enhanced images using the EfficientNet-B2 deep learning architecture.
  • Comparative analysis against established pre-trained models (VGG16, ResNet50, DenseNet, EfficientNet variants).

Main Results:

  • The proposed model achieved a diagnostic accuracy of 98.27%.
  • The model demonstrated superior performance compared to all benchmarked pre-trained models.
  • Effective differentiation was achieved between normal renal conditions and pathologies like tumors, stones, and cysts.

Conclusions:

  • The integrated MSF technique and EfficientNet-B2 model offer a powerful approach for accurate kidney disease diagnosis.
  • This research highlights the potential of advanced image enhancement coupled with deep learning for medical imaging.
  • The developed model presents a scalable solution for improving diagnostic accuracy in complex medical imaging scenarios.