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A kidney transplant is a surgical approach that involves replacing a non-functioning kidney with a healthy one from a donor. This procedure is often a treatment option for end-stage renal disease (ESRD) patients. The method requires careful recipient selection, including evaluating various medical and psychosocial factors. These criteria vary between transplant centers but generally include assessments of the patient's overall health, adherence to medical recommendations, and lifestyle...
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Preoperative ManagementThe primary goals of preoperative management in kidney transplantation are to optimize the patient’s metabolic state and prepare them for surgery through diet adjustments, necessary dialysis, and tailored medical treatment. This phase also involves comprehensive infection screening and patient education about the surgical procedure and postoperative care to improve outcomes and adherence.Medical ManagementA comprehensive evaluation is required for both the living...
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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 (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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Renal failure occurs when the kidneys lose their ability to filter waste products from the blood effectively. It can be classified into two types: acute renal failure (ARF) and chronic renal failure (CRF).
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Introduction:Acute Kidney Injury (AKI) describes a swift decrease in kidney function occurring over hours to days, characterized by the kidneys' failure to remove waste products from the bloodstream. This leads to dangerous complications like metabolic acidosis, fluid overload, and electrolyte imbalances, such as hyperkalemia, which can cause life-threatening arrhythmias. AKI is common in both hospital and outpatient settings, often triggered by dehydration, sepsis, or exposure to nephrotoxic...
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A two-stage renal disease classification based on transfer learning with hyperparameters optimization.

Mahmoud Badawy1,2, Abdulqader M Almars3, Hossam Magdy Balaha1,4

  • 1Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt.

Frontiers in Medicine
|April 24, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an AI framework using transfer learning to detect renal diseases early. The system accurately classifies kidney conditions from CT scans and histopathology images, achieving near-perfect accuracy.

Keywords:
AI-based diagnosisSparrow Search Algorithm (SpaSA)convolutional neural network (CNN)metaheuristic optimizationrenal diseases (RD)transfer learning (TL)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology
  • Nephrology

Background:

  • Renal diseases, including kidney stones and cancer, are significant global health concerns.
  • Early detection of renal diseases is crucial for effective treatment and improved patient outcomes.
  • Artificial intelligence (AI) offers promising tools for enhancing diagnostic accuracy in medical imaging.

Purpose of the Study:

  • To develop and evaluate an AI-based transfer learning framework for early detection and classification of renal diseases.
  • To assess the performance of various pre-trained convolutional neural network (CNN) models and optimization algorithms on renal imaging data.
  • To accurately classify renal disease types and tumor severity using CT scans and histopathological images.

Main Methods:

  • Utilized a transfer learning framework with pre-trained CNN models (VGG16, VGG19, Xception, DenseNet201, MobileNet, MobileNetV2, MobileNetV3Large, NASNetMobile).
  • Employed the Sparrow Search Algorithm (SpaSA) for optimizing model configurations.
  • Classified renal diseases into four categories (cyst, normal, stone, tumor) and tumor severity into five grades (0-4) using two distinct datasets.

Main Results:

  • DenseNet201 and MobileNet models demonstrated superior performance on the four-class dataset.
  • SGD Nesterov, AdaGrad, and AdaMax optimizers were recommended for model enhancement.
  • DenseNet201 and Xception achieved the best results for the five-class dataset, classifying tumor severity.
  • The proposed framework achieved high accuracy: 99.98% for four classes and 100% for five classes.

Conclusions:

  • The AI-based transfer learning framework effectively detects and classifies renal diseases with high accuracy.
  • The study highlights the potential of deep learning models, particularly DenseNet201 and Xception, in renal disease diagnosis.
  • The proposed framework surpasses existing state-of-the-art models, offering a reliable tool for early renal disease detection.