COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi

The diagnosis of COVID-19 is of vital demand. Several studies have been conducted to decide whether the chest X-ray and computed tomography (CT) scans of patients indicate COVID-19. While these efforts resulted in successful classification systems, the design of a portable and cost-effective COVID-1...

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Veröffentlicht in:PloS one Jg. 16; H. 5; S. e0250688
Hauptverfasser: Hosny, Khalid M., Darwish, Mohamed M., Li, Kenli, Salah, Ahmad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 11.05.2021
Public Library of Science (PLoS)
Schlagworte:
Accuracy
Algorithms
Biology and Life Sciences
Chest
Classification
Color imagery
Computed tomography
Computer and Information Sciences
Computer engineering
Computer science
Computers
Coronaviruses
COVID-19
Deep learning
Diagnostic imaging
Electrical engineering
Embedded systems
Engineering and Technology
Evaluation
Feature extraction
Image processing
Informatics
Linux
Medical diagnosis
Medical imaging
Medical imaging equipment
Medicine and Health Sciences
Methods
Object recognition
Physical Sciences
Research and Analysis Methods
Supervision
X rays
United States
China
Egypt
ISSN:1932-6203, 1932-6203
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Zusammenfassung:The diagnosis of COVID-19 is of vital demand. Several studies have been conducted to decide whether the chest X-ray and computed tomography (CT) scans of patients indicate COVID-19. While these efforts resulted in successful classification systems, the design of a portable and cost-effective COVID-19 diagnosis system has not been addressed yet. The memory requirements of the current state-of-the-art COVID-19 diagnosis systems are not suitable for embedded systems due to the required large memory size of these systems (e.g., hundreds of megabytes). Thus, the current work is motivated to design a similar system with minimal memory requirements. In this paper, we propose a diagnosis system using a Raspberry Pi Linux embedded system. First, local features are extracted using local binary pattern (LBP) algorithm. Second, the global features are extracted from the chest X-ray or CT scans using multi-channel fractional-order Legendre-Fourier moments (MFrLFMs). Finally, the most significant features (local and global) are selected. The proposed system steps are integrated to fit the low computational and memory capacities of the embedded system. The proposed method has the smallest computational and memory resources,less than the state-of-the-art methods by two to three orders of magnitude, among existing state-of-the-art deep learning (DL)-based methods.
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Competing Interests: The author(s) received no specific funding for this work.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0250688