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 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.