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A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images
Md Zabirul Islam1, Md Milon Islam1, Amanullah Asraf1
1Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh.
Insights
This study introduces a deep learning model combining convolutional neural networks (CNN) and long short-term memory (LSTM) for automatic COVID-19 detection from X-ray images. The advanced system achieved high accuracy, aiding in rapid and reliable disease diagnosis.
Area of Science:
- Medical Science
- Artificial Intelligence
- Radiology
Background:
- Automatic disease detection is critical for managing public health crises like the COVID-19 pandemic.
- Early and accurate diagnosis of COVID-19 is essential to reduce mortality rates and control its spread.
- Existing diagnostic methods can be time-consuming, necessitating faster automated solutions.
Purpose of the Study:
- To develop and evaluate a deep learning framework for the automated diagnosis of COVID-19 using X-ray imaging.
- To leverage the strengths of Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory (LSTM) networks for disease detection.
- To provide a rapid and accurate diagnostic tool to assist healthcare professionals in identifying COVID-19 cases.
Main Methods:
- A hybrid deep learning model integrating CNN and LSTM was designed for COVID-19 detection.
- CNN layers were employed for extracting relevant deep features from chest X-ray images.
- LSTM networks processed the extracted features to classify images as COVID-19 positive or negative.
Main Results:
- The proposed CNN-LSTM model demonstrated exceptional performance on a dataset of 4575 X-ray images (1525 COVID-19 positive).
- Achieved a diagnostic accuracy of 99.4%, Area Under the Curve (AUC) of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%.
- The results indicate the system's high efficacy in distinguishing COVID-19 cases from normal X-rays.
Conclusions:
- The developed deep learning system offers a highly accurate and efficient method for automated COVID-19 detection from X-ray images.
- This automated approach can significantly aid clinicians in timely diagnosis and patient management.
- Further improvements are anticipated with the availability of larger and more diverse datasets.
Abstract:
Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19) has become one of the most severe and acute diseases in recent times and has spread globally. Therefore, an automated detection system, as the fastest diagnostic option, should be implemented to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 4575 X-ray images, including 1525 images of COVID-19, were used as a dataset in this system. The experimental results show that our proposed system achieved an accuracy of 99.4%, AUC of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%. The system achieved desired results on the currently available dataset, which can be further improved when more COVID-19 images become available. The proposed system can help doctors to diagnose and treat COVID-19 patients easily.
