Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
Abstract
Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNN s) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and a deep CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 93.1% (with a sensitivity of 100%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases.
Read the paper
Related studies
- The potential and limitations of neutrons, electrons and X-rays for atomic resolution microscopy of unstained biological molecules
- X-ray-Computed Tomography Contrast Agents
- Reactivity of Surface Species in Heterogeneous Catalysts Probed by In Situ X-ray Absorption Techniques
- Single-crystal X-ray diffraction studies on structural transformations of porous coordination polymers
- Investigation of solid/vapor interfaces using ambient pressure X-ray photoelectron spectroscopy
- Resonant X-Ray Scattering Studies of Charge Order in Cuprates
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.