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Efficient deep learning approach for augmented detection of Coronavirus disease
Ahmed Sedik1, Mohamed Hammad2, Fathi E Abd El-Samie3,4
1Department of the Robotics and Intelligent Machines, Kafrelsheikh University, Kafrelsheikh, Egypt.
Neural Computing & Applications
|January 25, 2021
Summary
A new deep learning system using convolutional neural networks and convolutional long short-term memory effectively detects Coronavirus disease 2019 (COVID-19) from X-ray and CT scans, achieving 100% accuracy.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Infectious Diseases
Background:
- Coronavirus disease 2019 (COVID-19) diagnosis is challenged by limited annotated imaging data.
- Rapidly evolving COVID-19 statistics necessitate efficient diagnostic tools.
Purpose of the Study:
- To develop and validate a deep learning system for accurate COVID-19 detection.
- To address the challenge of limited annotated medical images for COVID-19 diagnosis.
Main Methods:
- Proposed deep learning models: Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (ConvLSTM).
- Utilized two datasets: one with CT images and another with X-ray images, including COVID-19, normal, and pneumonia categories.
- Tested models on separate CT, X-ray, and combined datasets.
Main Results:
- Achieved up to 100% accuracy and 100% F1 score in COVID-19 detection.
- Demonstrated high performance across CT, X-ray, and combined imaging datasets.
- Validated the system's ability to differentiate COVID-19 from pneumonia and normal cases.
Conclusions:
- The proposed deep learning models offer a promising solution for rapid COVID-19 screening.
- The system demonstrates high efficacy in detecting COVID-19 using both CT and X-ray imaging.
- Deep learning approaches are valuable for overcoming data limitations in infectious disease diagnosis.
