Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray

Abstract

The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.

Remotely Monitoring COVID-19 Patient Health Condition Using Metaheuristics Convolute Networks from IoT-Based Wearable Device Health Data

“…Irfan Ullah Khan et al [ 29 ] noted that, as the coronavirus pandemic (COVID-19) spreads around the world, it poses a serious threat to millions of people. Precise and appropriate treatment of COVID-19 is critical to halting its spread and reducing the risk of infection.…”

A Comprehensive Review of Machine Learning Used to Combat COVID-19

“…The dataset used contained a CXR of 64 COVID-19 positive patients and 57 patients with interstitial pneumonia. In [ 54 ], the authors proposed a join-fusion model which combines clinical data and CXR to increase the accuracy of the final prediction. CXR images are fed into EfficientNetB7 (which uses the ImageNet weights) which feeds to a global average pooling 2D layer before reaching the concatenation layer.…”

The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis

“…Some of these algorithms used for segmentation included U-Net, U-Net++, and V-Net, etc., while the ResNet and CNN models with inception were mostly used for classification [ 30 ]. The authors Khan et al [ 67 ], and Dhiman et al [ 68 ] utilized CXR alongside other clinical data with the aim of detecting and differentiating between COVID-19 patients from others. However, these studies used different approaches and algorithms to reach the same aim.…”