EARLY DETECTION OF COVID-19 IN LUNG X-RAYS USING AI ALGORITHMS (Eureka Journal of Health Sciences & Medical Innovation, 2026)
Early and accurate detection of COVID-19 is crucial for timely intervention, effective treatment, and controlling the spread of the virus. Chest X-ray imaging is widely used for assessing lung involvement in COVID-19 patients, but manual interpretation is time-consuming and prone to variability among radiologists. Artificial intelligence (AI) algorithms, particularly deep learning models, offer the potential for rapid, automated, and accurate detection of COVID-19 in chest X-rays. This paper reviews current AI-based approaches for COVID-19 detection, emphasizing convolutional neural networks (CNNs), transfer learning, and hybrid models. Performance metrics, clinical applicability, challenges such as dataset limitations and imaging variability, and future perspectives are discussed. The study highlights how AI-driven detection systems can support radiologists, optimize workflow efficiency, and improve patient care during the pandemic.
Keywords: COVID-19, chest X-ray, artificial intelligence, deep learning, convolutional neural networks, automated detection, radiology, pandemic response