A Novel Deep Learning Model for Enhancing Chest X-Ray Assessment using CXRIA-Net

A key diagnostic technique for recognizing several lung diseases gives pneumonia, TB, and COVID-19 chest X-ray (CXR) imaging is Manual interpreting CXR pictures, on the other hand, might be laborious and prone to inter-observer variation. This paper presents CXRIA-Net, a deep convolutional neural network (CNN) meant to improve chest X-ray picture assessment precision as well as effectiveness. Employing advanced deep learning technology, the approach simplifies feature extraction, enhances disease classifying, and reduces diagnostic mistakes. The above technique trains CXRIA-Net on a sizable dataset of identified chest X-ray images using transfer learning together with data augmenting to maximize performance. The model shows better precision, sensitiveness, and specificity in finding anomalies when compared to functioning state-of-the-art CNN architectures. Experimental investigations indicate that CXRIA-Net demonstrates excellent diagnostic precision capabilities which establish it as a promising instrument to assist radiologists and healthcare employees during medical imaging assessment. This paper demonstrates how deep learning integration with medical imaging technology reveals AI applications which benefit patient results and radiological task operations. The proposed CXRIA-Net model demonstrates excellent potential to help detect diseases early which enables better clinical decisions and operates with enhanced healthcare system efficiency.