COVID-19 Infection Map Generation and Detection from Chest X-Ray Images

Abstract

Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human-machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

References in corpus (14)

• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
• Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks
• Can AI help in screening Viral and COVID-19 pneumonia?
• Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
• COVID-19 Image Data Collection
• PadChest: A large chest x-ray image dataset with multi-label annotated reports
• Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification
• Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
• PDCOVIDNet: A Parallel-Dilated Convolutional Neural Network Architecture for Detecting COVID-19 from Chest X-Ray Images
• Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
• MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation
• A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening

Cited by in corpus (6)

• COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network
• OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images
• Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
• Reliable COVID-19 Detection Using Chest X-ray Images
• R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 Classification
• MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images