COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network

Abstract

Purpose Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage. Methods We used the COVID-19 Radiography database [36], which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model’s performance. From the external test dataset, we calculated the model’s accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity. Conclusions Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19.

Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans

“…Jain et al [ 28 ] used 490 COVID-19 and 5942 other images for classifying into three classes by the Xception model and achieved an accuracy of 97.97%. Nikolaou et al [ 68 ] used 3616 COVID-19 images for the two and three-class classification of images. They applied the EfficientNetB0 network and achieved an accuracy of 95% for two-class and 93% for three-class classification.…”

“…Five different matrices were utilized for the performance evaluation, namely: accuracy, precision, recall, F1-score, and area under the curve (AUC). The mathematical equations for each matrix are given in the equation below [ 28 , 60 , 68 , 69 ]: where TP: True Positive, TN: True Negative, FP: False Positive, and FN: False Negative.…”

A Novel Lightweight Approach to COVID-19 Diagnostics Based on Chest X-ray Images

“…ML-based methods cannot replace an experienced medical doctor in the final diagnosis, but they help significantly in the process, relieving the burden on health care and improving the diagnostic process. Screening with X-ray images is less expensive and faster than PCR testing [ 34 ]. This is one of the reasons why it is worth developing ML-based techniques to assist specialists in diagnostics.…”

Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models

“…The authors obtained accuracies of 98.58% for binary and 93.48% for the three-class experiment. Nikolaou et al [103] developed a novel CNN by modifying pre-trained EfficientNetB0. This network was applied for the binary (COVID-19 and normal) and three-class (COVID-19, pneumonia, and normal) classification, obtaining an accuracy of 95% for binary and 93% for three-class.…”