A Novel Deep Learning Model for Accurate Detection of COVID-19 and Pneumonia from Chest X-Rays Using ResPNet

Rapid detection and diagnosis of respiratory illnesses like COVID-19 and pneumonia are crucial for controlling outbreaks and managing public health resources. This paper presents a novel deep learning approach using convolutional neural networks (CNNs) to detect COVID-19 and pneumonia from chest X-rays. Leveraging a large dataset of annotated X-ray images, our method trains a model to distinguish between healthy individuals, COVID-19 patients, and those with pneumonia with high accuracy. The architecture of the proposed model, ResPNet, includes preprocessing steps, layer configurations, and activation functions optimized for this task. In our study, ResPNet demonstrated significant improvements over traditional image processing techniques and existing deep learning approaches, achieving 95% accuracy for normal, 94% for COVID-19, and 93% for pneumonia. Precision, recall, and F1-scores also indicated superior performance, highlighting the model's robustness across various image qualities and patient demographics. These results suggest that ResPNet is highly effective and reliable for real-time medical diagnostics. The model's integration of advanced architectural features and data augmentation techniques contributes to its high performance. Our study aims to enhance healthcare technology by providing a reliable and efficient tool for early and accurate disease detection. The successful implementation of ResPNet demonstrates its potential for integration into existing medical imaging workflows, ultimately aiding in timely patient management and resource allocation.