Refined Deep Learning Approaches for Enhanced COVID-19 Detection in Chest X-Ray Images

This paper presents a novel diagnostic platform based on deep convolutional neural networks (CNNs) to assist physicians and radiologists in detecting COVID-19 from chest X-ray images. By providing a reliable second opinion, this platform aims to improve diagnostic accuracy and efficiency compared to traditional methods. The proposed framework leverages advanced preprocessing techniques to enhance image quality and model generalization. It investigates a two-stage preprocessing pipeline involving an adaptive median filter for noise reduction and image enhancement, followed by data augmentation and normalization to address class imbalance. It then employs multiple pre-trained CNN architectures, validated on an extensive and diverse data set. Rigorous validation demonstrates the robustness and generalizability of the models, addressing key challenges in reproducibility and reliability for real-world clinical applications. This innovative tool holds great promise for accelerating and improving the accuracy of COVID-19 diagnosis, aiding healthcare professionals in effectively managing future crises as new variants of the virus continue to emerge.