Automated Report Generation: A GRU Based Method for Chest X-Rays
Wajahat Akbar, Muhammad Inam Ul Haq, Abdullah Abdullah, Sher Muhammad Daudpota, Ali Shariq Imran, Mohib Ullah
Abstract
Radiology reports are the primary medium through which physicians communicate with patients and share diagnoses from medical scans. Examples include radiology reports for chest X-Rays and CT scans. Chest X-Ray images are frequently employed in clinical screening and diagnosis. However, writing medical reports for the X-Ray is tedious, error-prone, and time-consuming, even for experienced radiologists. The modern world of clinical practice demands that a radiologist with specialized training manually evaluate chest X-Ray and report the findings. Therefore, this paper explores the ability of artificial intelligence (AI) to automate diagnosing diseases through chest X-Rays and accurately generate radiology reports to alleviate the burdens of medical doctors. Automating this manual process could streamline a clinical workflow, and healthcare quality could be improved. The conventional AI-based abstract methods provide fluent but clinically incorrect radiology reports. The proposed Gated Recurrent Unit (GRU) based model provides both stan-dard language generation and clinical coherence. The model is evaluated on the Indiana University dataset with commonly-used metrics BLEU and ROUGE-L. Empirical evaluations illustrate that the proposed approach can make more precise diagnoses and generate more fluent and precise reports than existing baselines.