Automated Radiology Report Generation from Chest X-ray Scans Using Deep Learning

A chest X-ray is a common diagnostic tool for many thoracic illnesses. Interpreting these images and coming up with accurate diagnostic results is a difficult and time-consuming task for radiologists. Recent results using deep learning approaches to automate the analysis of chest X-rays are promising. This paper provides a comprehensive description of deep learning techniques used to chest X-ray processing for the diagnosis of several diseases, including TB, pneumonia, and COVID-19. This paper provide a summary of the primary deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention models, that are used for disease classification, localization, segmentation, and report generation from chest X-rays. This paper also discuss explainability techniques that are used to offer transparency and increase trust in model predictions. Along with existing issues, future directions are taken into consideration, such as multimodal learning, few-shot learning, and model evaluation. This paper explains the model which takes less time and give the optimal results. In order to facilitate future research into developing AI systems that are more accurate, understandable, and advantageous from a medical standpoint, this article provides an overview of the most current developments in deep learning techniques for automated chest X-ray diagnosis.