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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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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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The diagnosis of renal calculi involves several imaging techniques, including non-contrast CT scans and ultrasound. These methods help visualize kidney stones, assess their size and location, and detect possible obstructions. Additionally, Measuring urine pH is useful for diagnosing specific stone types, such as struvite (alkaline pH) and uric acid stones (acidic pH). Cystine stones are primarily linked to cystinuria, a genetic condition. A urinalysis helps detect blood in the urine (hematuria)...
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IntroductionIntravenous Urography (IVU) and Retrograde Pyelography (RP) are important diagnostic imaging techniques used to evaluate the urinary system. These methods help identify structural abnormalities, obstructions, and functional issues in the kidneys, ureters, and bladder. Both procedures use iodine-based contrast media to enhance the visibility of urinary tract structures on X-ray images, though they differ in their methods and indications.1. Intravenous Urography (IVU)Intravenous...
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Procedures for Kidney StonesMedical intervention is necessary when kidney stones or renal calculi are too large to pass spontaneously (typically greater than 5 millimeters) when stones are accompanied by symptomatic infection (such as fever or pyelonephritis), when they impair kidney function, or when they cause persistent symptoms like severe pain, nausea, or urinary retention. Additionally, patients with only one kidney or those who cannot be treated with medical management also require...
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Imaging Studies II: Ultrasonography01:24

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IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
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Enhanced CT image classification for kidney stones using pruned ConvNeXt and two-tier optimization.

B Reuben1, C Narmadha1, C Rajanandhini1

  • 1Department of ECE, Periyar Maniammai Institute of Science & Technology (Deemed to be University), Thanjavur, India.

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Summary

This study introduces a novel deep learning model for kidney stone detection in CT scans, achieving 97.8% accuracy. The innovative approach enhances diagnostic efficiency and accuracy for kidney stone identification.

Keywords:
ConvNeXtDeep learning modelchannel pruning pufferfish optimization algorithm (POA)kidney stone

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Kidney stone detection in CT images is crucial for diagnosis and treatment planning.
  • Current methods may face challenges in accuracy and efficiency.
  • Deep learning offers potential for improved medical image analysis.

Purpose of the Study:

  • To propose an innovative two-tier feature-optimized deep learning model for kidney stone detection.
  • To enhance the accuracy and efficiency of kidney stone identification in CT images.
  • To leverage advanced deep learning architectures and optimization algorithms.

Main Methods:

  • A modified ConvNeXt learning model was employed for complex feature extraction from CT images.
  • Dynamic Channel Pruning was utilized within the ConvNeXt architecture to retain informative channels.
  • The Pufferfish Optimization Algorithm (POA) was applied for optimal feature selection and LightGBM classifier hyperparameter tuning.

Main Results:

  • The proposed model achieved a high accuracy of 97.8% in kidney stone detection.
  • The two-tier optimization strategy significantly improved detection performance compared to other models.
  • The model demonstrated enhanced efficiency in processing CT images for kidney stone identification.

Conclusions:

  • The developed deep learning model offers a promising solution for accurate and efficient kidney stone detection.
  • Dynamic Channel Pruning and Pufferfish Optimization are key components contributing to the model's success.
  • This approach has the potential to improve clinical diagnosis and patient management for kidney stones.