COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection

By Jianpeng Zhang, Yutong Xie, Yi Li, Chunhua Shen, Yong Xia. 2020

464 citations recorded by the source corpus.

Coronaviruses are important human and animal pathogens. To date the novel COVID-19 coronavirus is rapidly spreading worldwide and subsequently threatening health of billions of humans. Clinical studies have shown that most COVID-19 patients suffer from the lung infection. Although chest CT has been shown to be an effective imaging technique for lung-related disease diagnosis, chest Xray is more widely available due to its faster imaging time and considerably lower cost than CT. Deep learning, one of the most successful AI techniques, is an effective means to assist radiologists to analyze the vast amount of chest X-ray images, which can be critical for efficient and reliable COVID-19 screening. In this work, we aim to develop a new deep anomaly detection model for fast, reliable screening. To evaluate the model performance, we have collected 100 chest X-ray images of 70 patients confirmed with COVID-19 from the Github repository. To facilitate deep learning, more data are needed. Thus, we have also collected 1431 additional chest X-ray images confirmed as other pneumonia of 1008 patients from the public ChestX-ray14 dataset. Our initial experimental results show that the model developed here can reliably detect 96.00% COVID-19 cases (sensitivity being 96.00%) and 70.65% non-COVID-19 cases (specificity being 70.65%) when evaluated on 1531 Xray images with two splits of the dataset.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices (2020, background): “Obtained sensitivity of 97.5% and specificity of 90%. and c) anomaly detection head [31] X-ray 100 chest images of COVID-19 positive cases [18] and 1431 images of pneumonia [32] The sensitivity of 96.00% and specificity of 70.65% is achieved.”
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 (2020, methodology): “[75] present a ResNet based model to detect COVID-19 from X-ray images.”
Deep learning based detection and analysis of COVID-19 on chest X-ray images (2020, unknown): “In [43], the authors talked about the implementation of a deep anomaly detection method for reliable screening of Covid-19 patients.”
Using X-ray images and deep learning for automated detection of coronavirus disease (2020, background): “In Zhang et al. (2020), the authors presented a deep learning model allowed to detect Covid19 from healthy people using Chest X-ray images.”
JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation (2020, background): “This is based on the fact that the clinical signs of chest X-rays for most COVID-19 patients indicate lung infection [6].”
Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases (2021, unknown): “[36] detected COVID-19 from chest X-rays using deep learning on a small dataset.”
COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data (2020, methodology): “A custom-built 18-layer residual network pre-trained on the ImageNet weights against COVID-19 (100 images) and Pneumonia (1431 images) X-Ray image datasets [54].”
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, methodology): “th remarkable results [16]. Based on chest X-ray images, Zhang et al. aimed to develop a deep learning-based model that can detect COVID-19 with high sensitivity, providing fast and reliable scanning [17]. Singh et al. classified the chest computed tomography (CT) images from infected people with an”
AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app (2020, background): “To this end, the medical researcher in our team began by analyzing in-depth the pathomorphological changes caused by COVID-19 in respiratory system by examining the data reported in numerous recent X-rays [13], [18]–[20] and CTscans based studies [14], [21], [22] of COVID-19 patients.”
AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system (2020, unknown): “And Zhang et al. [46] present a ResNet based model to detect COVID-19 lesions from X-ray images.”
Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning (2020, methodology): “Zhang et al. (2020) used DenseNet to detect the coronavirus infection in the patients.”