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Updated: Aug 10, 2025
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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
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DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances
Md Khabir Uddin Ahamed1, Md Manowarul Islam1, Md Ashraf Uddin1,2
1Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh.
Diagnostics (Basel, Switzerland)
|February 11, 2023
Summary
A new deep learning model using ResNet50V2 architecture effectively detects COVID-19 from chest X-rays. This AI tool aids in rapid diagnosis, especially in resource-limited settings, achieving high accuracy.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Radiology
Background:
- COVID-19 (Coronavirus Disease 2019) poses a global health and economic challenge.
- Many developing nations face difficulties in widespread COVID-19 detection due to resource scarcity.
- There is a critical need for efficient diagnostic tools to support healthcare professionals.
Purpose of the Study:
- To develop and investigate an efficient deep learning model for COVID-19 detection using chest X-ray images.
- To provide timely assistance to clinicians and radiologists in diagnosing COVID-19 cases.
- To address the limitations of testing kit availability in resource-constrained environments.
Main Methods:
- A deep learning model based on the ResNet50V2 architecture was proposed.
- The ResNet50V2 architecture was enhanced with six additional layers for improved performance.
- Grad-CAM was utilized for discriminative localization to interpret radiological image detection.
Main Results:
- The model achieved 99.51% accuracy for four-class classification (COVID-19/normal/bacterial pneumonia/viral pneumonia) on Dataset-2.
- High accuracies were also obtained for three-class (96.52%) and two-class (99.13%) classifications on Dataset-1.
- The model demonstrated robust performance across different classification tasks.
Conclusions:
- The proposed deep learning model offers an efficient method for detecting COVID-19 from chest X-rays.
- The high accuracy and interpretability of the model can significantly aid radiologists in rapid diagnosis.
- This AI-driven approach can help overcome resource limitations in COVID-19 testing.
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