Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images

Abstract

The use of computer-aided diagnosis in the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the medical infrastructure. Chest X-ray (CXR) imaging has several advantages over other imaging techniques as it is cheap, easily accessible, fast and portable. This paper explores the effect of various popular image enhancement techniques and states the effect of each of them on the detection performance. We have compiled the largest X-ray dataset called COVQU-20, consisting of 18,479 normal, non-COVID lung opacity and COVID-19 CXR images. To the best of our knowledge, this is the largest public COVID positive database. Ground glass opacity is the common symptom reported in COVID-19 pneumonia patients and so a mixture of 3616 COVID-19, 6012 non-COVID lung opacity, and 8851 normal chest X-ray images were used to create this dataset. Five different image enhancement techniques: histogram equalization, contrast limited adaptive histogram equalization, image complement, gamma correction, and Balance Contrast Enhancement Technique were used to improve COVID-19 detection accuracy. Six different Convolutional Neural Networks (CNNs) were investigated in this study. Gamma correction technique outperforms other enhancement techniques in detecting COVID-19 from standard and segmented lung CXR images. The accuracy, precision, sensitivity, f1-score, and specificity in the detection of COVID-19 with gamma correction on CXR images were 96.29%, 96.28%, 96.29%, 96.28% and 96.27% respectively. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11 %, 94.55 %, 94.56 %, 94.53 % and 95.59 % respectively for segmented lung images. The proposed approach with very high and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.

arXiv comment34 pages, 6 Tables, 11 Figures

References in corpus (17)

• Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
• Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
• Can AI help in screening Viral and COVID-19 pneumonia?
• CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
• COVID-19 Image Data Collection
• SmoothGrad: removing noise by adding noise
• Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization
• Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray
• COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs
• Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis
• GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID-19 Disease based on Gravitational Search Optimization Algorithm
• Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
• ECOVNet: An Ensemble of Deep Convolutional Neural Networks Based on EfficientNet to Detect COVID-19 From Chest X-rays
• Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
• COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays
• Intra-model Variability in COVID-19 Classification Using Chest X-ray Images