• Patrick Timothy
• Er. Priyanka Jarial

Chest radiography is widely used in medicine because it is inexpensive and available. It is used to diagnose various diseases, such as pneumonia, tuberculosis, and lung cancer. Chest X-ray interpretation is challenging due to overlapping structures, radiologist workload, and a lack of a consistent level of skills. Deep learning, a subfield of machine learning, has been widely used to automate feature extraction and classification in medical imaging. Chest X-ray (CXR) analysis, based on Convolutional Neural Networks (CNNs), has become a significant task to detect and classify diseases directly from the image. The feasibility of using deep learning for chest X-ray analysis was initially demonstrated on large publicly available datasets like ChestX-ray14 and CheXpert. Pretrained models like CheXNet also demonstrated near-par with human radiologists. In recent years, research has shifted from binary classification to multi-label disease diagnosis, disease localization, and anatomical segmentation using U-Net architectures. Challenges such as limited availability of training data for different diseases are overcome by generative adversarial networks (GANs), few-shot learning, and self-supervised methods. Methods to reduce the reliance on large amounts of labeled data have also been studied. Interpretability of AI algorithms and models is an important area of research. Explainable AI (XAI) methods like Grad-CAM are used to highlight image regions and justify decisions made by the AI models. However, such methods still require improvement to reach clinically sufficient precision. Fairness and generalization to different populations are also challenges, as models trained on homogeneous datasets tend to be biased and may result in inequitable performance when deployed. Lightweight architectures like MobileNet have also been explored to facilitate deployment on edge devices with limited computational resources. Despite promising results, some limitations and challenges remain, including dataset bias, lack of external validation, and workflow integration. The future of AI in chest X-ray analysis is focused on building robust, transparent, and clinically validated models that can assist radiologists, increase efficiency, and improve patient outcomes on a global scale.

Deep Learning; Chest X-rays; Pneumonia Detection; Convolutional Neural Networks; Explainable AI; Medical Imaging; Transfer Learning

"AI in Healthcare: Studying Chest Images Using Deep Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 9, page no.f66-f71, September-2025, Available :http://www.jetir.org/papers/JETIR2509510.pdf

"AI in Healthcare: Studying Chest Images Using Deep Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 9, page no. ppf66-f71, September-2025, Available at : http://www.jetir.org/papers/JETIR2509510.pdf