Abstract
Machine Learning (ML) techniques have been combined with modern technologies across medical fields to detect and diagnose many diseases. Meanwhile, given the limited and unclear statistics on the Coronavirus Disease 2019 (COVID-19), the greatest challenge for all clinicians is to find effective and accurate methods for early diagnosis of the virus at a low cost. Medical imaging has found a role in this critical task utilizing a smart technology through different image modalities for COVID-19 cases, including X-ray imaging, Computed Tomography (CT) and magnetic resonance image (MRI) that can be used for diagnosis by radiologists. This paper combines ML with imaging analysis in an artificial deep learning approach for COVID-19 detection. The proposed methodology is based on convolutional long short term memory (ConvLSTM) to diagnose COVID-19 automatically from X-ray images. The main features are extracted from regions of interest in the medical images, and an intelligent classifier is used for the classification task. The proposed model has been tested on a dataset of X-ray images for COVID-19 and normal cases to evaluate the detection performance. The ConvLSTM model has achieved the desired results with high accuracy of 91.8%, 95.7%, 97.4%, 97.7% and 97.3% at 10, 20, 30, 40 and 50 epochs that will detect COVID-19 patients and reduce the medical diagnosis workload.
Highlights
In December 2019, the COVID-19 pandemic emergency was identified in China and is still spreading from human to human rapidly worldwide
Tab. 1 depicts the outcomes of the proposed ConvLSTM modality at various numbers of epochs of 10, 20, 30, 40 and 40 for True Negative Ratio (TNR), False Negative Ratio (FNR), True Positive Ratio (TPR), False Negative Ratio (FPR), accuracy and testing time
The simulation outcomes demonstrate that the presented ConvLSTM modality has an accuracy of 91.8%, 95.7%, 97.4%, 97.7% and 97.3% at 10, 20, 30, 40 and 50 epochs respectively
Summary
In December 2019, the COVID-19 pandemic emergency was identified in China and is still spreading from human to human rapidly worldwide. In addition to its public health implications, it is a substantial financial burden for all countries on top of the problem surrounding the low efficiency of vaccinations and the difficulty of obtaining them. The detection of COVID-19 at an early stage plays a vital role in controlling and transmitting the virus. According to the Chinese government guidelines [1], the virus diagnosis by gene sequencing for blood or respiratory samples is the main pointer for reverse transcription-polymerase chain reaction (RT-PCR). The RT-PCR process takes a long time to get results compared to the rapid spread rate of COVID-19, which means many infected patients go undetected. Several efforts have been made to search for alternative methods to mitigate the current inefficiency and scarcity of COVID-19 testing
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