Covid-19 Detection From X-Ray Images Using CNN

Abstract

Controlling the spread of COVID-19 and guaranteeing a timely medical response depend on early and precise identification. Even if conventional diagnostic techniques like RT-PCR work well, they frequently take a lot of time and money. This study presents a deep learning-based method that uses convolutional neural networks (CNNs) to detect COVID-19 from chest X-ray pictures. By automatically extracting and learning hierarchical features from input photos, CNNs outperform human feature engineers in image categorization.To train the CNN model for precise classification, the suggested approach makes use of a sizable, labeled dataset of chest X-ray pictures, which includes images of healthy lungs, COVID-19 instances, and pneumonia. Preprocessing techniques like data augmentation and normalization are used to enhance model performance and reduce overfitting. Consequently, the trained CNN exhibits good levels of sensitivity, specificity, and accuracy in detecting COVID-19 instances, providing radiologists and healthcare professionals with a useful tool.
By automating the screening process, this technology aids early detection, reduces the workload for medical professionals, and speeds up diagnosis—all of which are very helpful in environments with limited resources. This project's integration of AI with medical imaging demonstrates the revolutionary potential of deep learning in pandemic response and contemporary healthcare diagnostics. The system's effectiveness as a helpful diagnostic tool is demonstrated by experimental findings, which show that it can consistently differentiate COVID-19 from other illnesses. This method can improve diagnostic speed and reliability when included into clinical procedures, particularly in high-demand healthcare settings.