A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

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.

EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers

“…Extensive experiments presented 96% accuracy for this system. Many researchers are currently working to build a deep neural network-based automated COVID-19 detection system [ 19 ]. They have developed several deep neural network models for feature extraction from COVID-19-infected chest X-ray images, thereby ensuring good accuracy, sensitivity, and specificity.…”

Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

“…Following this idea, a large number of researchers have been working on the issue of automatic classification of COVID-19 from CXR images [10,11,12,13,14,15,16,17,18]. These studies report systems with high performance rates.…”

Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images

“…Although the false predictions in MobileNetV2 (Apostolopoulos and Mpesiana 2020 ) and Dark COVID-Net (Ozturk et al 2020 ) were slightly lower than the proposed model, however, the model lacked with an approximately 5% and 12% lower accuracy. Islam et al ( 2020 ) proposed a novel approach using CNN as a feature extractor and LSTM for detection and their approach closely follows behind our proposed ensemble framework with an accuracy of 97%. Hence, the proposed ensemble for the multi-classification task outperformed various other approaches with a final classification accuracy of 99.21%.…”