In the fight against COVID-19, this study explores clinical image processing and deep learning for effective solutions. Emphasizing collaboration between scientists and policymakers, it addresses data reliability issues and sparse experimentation, critical for accurate COVID-19 identification and mitigation of underreported cases. The proposed Henry Gas Solubility (HGS) optimized Deep Convolutional Neural Network (DCNN) enhances prediction accuracy and computational efficiency, validated through rigorous experiments. The study highlights the importance of integrating diverse datasets and outlines future research directions, highlighting its potential impact on healthcare decision-making and pandemic response strategies.