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Radiological Investigation I: X-ray and CT

Radiological Investigation I: X-ray and CT

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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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

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Published on: December 19, 2020

Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays.

N Narayan Das¹, N Kumar², M Kaur³

¹Department of Information Technology, School of Computing and Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India.

Ingenierie Et Recherche Biomedicale : IRBM = Biomedical Engineering and Research

|August 25, 2020

Summary

This summary is machine-generated.

This study introduces an automated deep learning approach using chest X-rays for COVID-19 detection. The Xception model significantly improves accuracy over existing methods, addressing limitations of real-time polymerase chain reaction (RT-PCR) testing.

Keywords:

COVID-19 Chest x-ray Deep learning Transfer learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Infectious Disease Diagnostics

Background:

Real-time polymerase chain reaction (RT-PCR) is the standard for COVID-19 detection but is slow, costly, and prone to false negatives.
Radiological imaging, particularly chest X-rays, offers an alternative for COVID-19 diagnosis due to accessibility and lower radiation compared to CT scans.
Manual analysis of chest X-rays for COVID-19 signatures is time-consuming and error-prone, necessitating automated solutions.

Purpose of the Study:

To develop an automated deep transfer learning approach for detecting COVID-19 infection using chest X-ray images.
To leverage the Xception model for enhanced COVID-19 detection accuracy and efficiency.
To provide a faster and more reliable diagnostic tool compared to traditional methods.

Main Methods:

Utilized chest X-ray images as the primary data source for COVID-19 detection.
Implemented a deep transfer learning strategy employing the Xception model.
Trained and fine-tuned the Xception network on relevant datasets for optimal performance.

Main Results:

The proposed automated Xception model demonstrated significantly superior performance in COVID-19 detection from chest X-rays.
Achieved higher accuracy and potentially reduced analysis time compared to existing diagnostic models.
Validated the effectiveness of deep transfer learning for analyzing radiological signatures of COVID-19.

Conclusions:

Automated analysis of chest X-rays using deep transfer learning, specifically the Xception model, is a viable and effective method for COVID-19 detection.
This approach offers a promising alternative to RT-PCR, improving diagnostic speed and accuracy.
Further research into deep learning for medical image analysis can enhance infectious disease diagnostics.