Chest Xray Medical Diagnosis with Deep Learning

Abstract

Detecting Disease in Chest X-Rays with Deep Learning Chest X-rays (CXRs) is a common and vital diagnostic tool, but interpreting them accurately requires trained radiologists who can be scarce in many areas. This research explores the potential of deep learning (DL), a type of artificial intelligence (AI), to automatically detect diseases in CXRs. The review explains the basics of DL for CXR analysis, including how deep neural networks (DNNs) work and how transfer learning and data augmentation techniques can improve their performance. It then examines recent studies on applying DNNs to identify common CXR abnormalities like lung nodules, pneumonia, and pneumothorax. This includes exploring multi-class classification, where the model can detect multiple diseases simultaneously. The abstract compares the performance of these techniques with human observers and discusses the challenges of implementing DNN models in clinical practice, particularly their relationship with radiologists. Overall, the research investigates how DL can potentially improve disease detection using CXRs, addressing the need for accurate and accessible diagnostic tools. Key Words: Chest X-rays, Deep Learning, Disease Detection, Artificial Intelligence, Neural Networks