Abstract
Objectives: Since late 2019, Coronavirus Disease 2019 (COVID-19) has spread around the world. It has been determined that the disease is very contagious and can cause Acute Respiratory Distress (ARD). Medical imaging has the potential to help identify, detect, and quantify the severity of this infection. This work seeks to develop a novel auto-detection technique for verified COVID-19 cases that can detect aberrant alterations in traditional X-ray pictures.
Methods: Nineteen separately colored layers were created from X-ray scans of patients diagnosed with COVID-19. Each layer represents objects that have a similar contrast and can be represented by a single color. In a single layer, objects with similar contrasts are formed. A single color image was created by extracting all the objects from all the layers. The prototype model could recognize a wide range of abnormal changes in the image texture based on color differentiation. This was true even when the contrast values of the detected unclear abnormalities varied slightly.
Results: The results indicate that the proposed novel method is 91% accurate in detecting and grading COVID-19 lung infections compared to the opinions of three experienced radiologists evaluating chest X-ray images. Additionally, the method can be used to determine the infection site and severity of the disease by categorizing X-rays into five severity levels.
Conclusion: By comparing affected tissue to healthy tissue, the proposed COVID-19 auto-detection method can identify locations and indicate the severity of the disease, as well as predict where the disease may spread.
Keywords: Auto-detection, SARS-COV-2, chest X-ray images, lung infection, disease severity, ELISA.
Graphical Abstract
[http://dx.doi.org/10.1016/j.chaos.2020.109942] [PMID: 32834576]
[http://dx.doi.org/10.1016/j.cyto.2020.155190] [PMID: 32673995]
[http://dx.doi.org/10.1021/acs.jpcb.0c04553] [PMID: 32559081]
[PMID: 32904528]
[http://dx.doi.org/10.1016/j.idm.2020.08.001] [PMID: 32835146]
[http://dx.doi.org/10.3390/healthcare8020099] [PMID: 32326512]
[http://dx.doi.org/10.1186/s41747-020-00167-0] [PMID: 32592118]
[http://dx.doi.org/10.1016/j.joim.2020.03.004] [PMID: 32307268]
[http://dx.doi.org/10.1016/j.eclinm.2020.100463] [PMID: 32838236]
[PMID: 32880993]
[http://dx.doi.org/10.1148/ryct.2020200322] [PMID: 33778612]
[http://dx.doi.org/10.3390/genes11091002] [PMID: 32858958]
[http://dx.doi.org/10.1007/s00330-020-07032-z] [PMID: 32621237]
[http://dx.doi.org/10.1109/TCSVT.2020.2981530]
[http://dx.doi.org/10.1049/iet-ipr.2019.1481]
Call for Papers in Thematic Issues
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging
Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
Multimodal Imaging Using Spectroscopic Techniques for Improved Surgical Guidance
The majority of patients with solid malignancies have surgical excision as their primary and most successful method of medical care. On the other hand, patients experience recurrence and subsequent recurrence. Their objective was to provide a simpler and more concise explanation of the obstacles faced by physicians and the ways ...read more
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, ...read more
16
1