Classification of COVID-19 from Chest X-Ray Images Using a Machine Vision Technique

This paper proposes a disciplined image-enhancement-then-classification pipeline for COVID-19 chest X-ray screening. Its key contribution is objectively validating enhancement filters — comparing HE, AHE, and CLAHE using full-reference IQA metrics (MSE, RMSE, PSNR) — rather than relying on subjective filter choice, as prior work does. CLAHE (clip limit 1, 8×8 tiles) was identified as optimal, and a CNN trained on 400 CLAHE-enhanced images with ReLU activation achieved 0% classification error, outperforming prior COVID-19 classifiers (89–95% accuracy) that skip explicit, quantified enhancement.