Advancing Sustainable COVID-19 Diagnosis: Integrating Artificial Intelligence with Bioinformatics in Chest X-ray Analysis

Responding to the critical health crisis triggered by respiratory illnesses, notably COVID-19, this study introduces an innovative and resource-conscious methodology for analyzing chest X-ray images. We unveil a cutting-edge technique that marries neural architecture search (NAS) with genetic algori...

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Bibliographic Details
Published in:Information (Basel) 2024-04, Vol.15 (4), p.189
Main Authors: Louati, Hassen, Louati, Ali, Lahyani, Rahma, Kariri, Elham, Albanyan, Abdullah
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Bioinformatics
Comparative studies
Computer architecture
Diagnostic imaging
genetic algorithm
Genetic algorithms
Health care
Innovations
Medical imaging
Medical imaging equipment
Neural networks
optimization in AI diagnostics
sustainable healthcare solutions
transfer learning
X ray analysis
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Summary:Responding to the critical health crisis triggered by respiratory illnesses, notably COVID-19, this study introduces an innovative and resource-conscious methodology for analyzing chest X-ray images. We unveil a cutting-edge technique that marries neural architecture search (NAS) with genetic algorithms (GA), aiming to refine the architecture of convolutional neural networks (CNNs) in a way that diminishes the usual demand for computational power. Leveraging transfer learning (TL), our approach efficiently navigates the hurdles posed by scarce data, optimizing both time and hardware utilization—a cornerstone for sustainable AI initiatives. The investigation leverages a curated dataset of 1184 COVID-positive and 1319 COVID-negative chest X-ray images, serving as the basis for model training, evaluation, and validation. Our methodology not only boosts the precision in diagnosing COVID-19 but also establishes a pioneering standard in the realm of eco-friendly and effective healthcare technologies. Through comprehensive comparative analyses against leading-edge models, our optimized solutions exhibit significant performance enhancements alongside a minimized ecological impact. This contribution marks a significant stride towards eco-sustainable medical imaging, presenting a paradigm that prioritizes environmental stewardship while adeptly addressing modern healthcare exigencies. We compare our approach to state-of-the-art architectures through multiple comparative studies.
ISSN:2078-2489
2078-2489
DOI:10.3390/info15040189