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Related Concept Videos

Radiological Investigation I: X-ray and CT01:30

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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Related Experiment Video

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques.

Nadiah Baghdadi1, Ahmed S Maklad2,3, Amer Malki2

  • 1Nursing Management and Education Department, College of Nursing, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Sensors (Basel, Switzerland)
|May 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an AI method using EfficientNet-B4 to differentiate sarcoidosis from tuberculosis (TB) on X-rays, achieving high accuracy. Reinhard stain normalization significantly improved diagnostic performance, offering a cost-effective solution.

Keywords:
EfficientNetschest X-rayspulmonary sarcoidosissarcoidosis detectionstain normalizationtuberculosis

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Pulmonary Diseases

Background:

  • Sarcoidosis and tuberculosis (TB) share similar radiological features, leading to frequent misdiagnosis and mistreatment.
  • Current diagnostic methods like biopsies are invasive, costly, and time-consuming.
  • There is a need for accurate, non-invasive methods to distinguish between sarcoidosis and TB.

Purpose of the Study:

  • To develop and evaluate a computer-aided diagnosis (CAD) system for differentiating pulmonary sarcoidosis from TB using chest X-ray images.
  • To compare the performance of different EfficientNet models for this classification task.
  • To investigate the impact of stain normalization techniques on diagnostic accuracy.

Main Methods:

  • Utilized a dataset of 231 sarcoidosis, 563 TB, and 1010 normal chest X-ray images.
  • Fine-tuned and compared seven EfficientNet designs for image classification.
  • Applied Reinhard and Macenko stain normalization techniques and assessed their effect on model performance.

Main Results:

  • The EfficientNet-B4 model, with Reinhard stain normalization, achieved 98.56% accuracy, 98.36% sensitivity, and 98.67% precision.
  • Macenko stain normalization resulted in slightly lower performance metrics (97.21% accuracy, 96.9% sensitivity, 97.11% precision).
  • Reinhard stain normalization demonstrated superior performance in enhancing EfficientNet-B4's classification accuracy.

Conclusions:

  • The proposed AI framework, particularly EfficientNet-B4 with Reinhard stain normalization, shows significant potential for accurate and efficient differentiation of pulmonary sarcoidosis from TB.
  • This computer-aided diagnosis method can improve diagnostic efficiency, reduce costs, and minimize patient discomfort associated with invasive procedures.
  • The findings suggest the clinical utility of this AI approach in diagnosing pulmonary sarcoidosis.