학술논문

Deep Learning based Automatic Radiology Report Generation
Document Type
Conference
Source
2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS) Artificial Intelligence and Smart Energy (ICAIS), 2023 Third International Conference on. :1521-1526 Feb, 2023
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Deep learning
Heart
Lung
Radiology
Turning
Feature extraction
Decoding
Chest x-rays
Radiologist
Encoder
Decoder
Attention
Bilingual Evaluation Understudy
Chexnet
Language
Abstract
The diagnostic x-ray examination is carried out using the chest x-ray. It is the responsibility of the radiologist to analyze the x-rays and draw conclusions from them to prescribe the proper care. Obtaining comprehensive medical reports from these x-rays is frequently time-consuming. Images of the heart, lungs, airways, spine, and chest bones can be seen in a chest x-ray. A radiologist may see thousands of x-ray images in populous nations. The goal of this project is to present a collection of the best deep-learning techniques for producing medical reports from X-ray images automatically. Deep learning algorithms have been used with models to handle this difficult task and produce correct results. Therefore, a lot of work and time can be saved if a properly trained deep learning model can generate these medical reports automatically. In this research, the text report is produced using an encoder and decoder with an attention model, while the image features are obtained using a pretrained CheXnet model. The BLEU score is used to evaluate the resulting text report.