ArticlePloS one2021
COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- RETRACTED: Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.PloS one · 2024Article
- Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation.JMIR formative research · 2023Article
- An Artificial Intelligence-as-a-Service Architecture for deep learning model embodiment on low-cost devices: A case study of COVID-19 diagnosis.Applied soft computing · 2023Article
- A hybrid CNN and ensemble model for COVID-19 lung infection detection on chest CT scans.PloS one · 2023Article
- CXGNet: A tri-phase chest X-ray image classification for COVID-19 diagnosis using deep CNN with enhanced grey-wolf optimizer.Biomedical signal processing and control · 2022Article
- A deep and handcrafted features-based framework for diagnosis of COVID-19 from chest x-ray images.Concurrency and computation : practice & experience · 2022Article
- CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network.Wireless personal communications · 2022Article
- A Robust UWSN Handover Prediction System Using Ensemble Learning.Sensors (Basel, Switzerland) · 2021Article
- Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models.Computational intelligence and neuroscience · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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