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However, the manual process of detecting diseases and writing their radiology reports is labor-intensive and time-consuming for radiologists. As a result, there has been a growing interest in the development of automated systems for generating radiological chest X-ray (CXR) reports. Although previous research has concentrated on improving the quantitative performance of these automated reports, the quality of the reports has often been neglected. Radiology reports are crucial for communicating with physicians and patients. Therefore, developing automated systems to generate these reports can significantly reduce radiologists\u2019 workload and enhance efficiency in clinical practice by automating the diagnosis of CXR through artificial intelligence (AI) and generating CXR reports. This paper aims to ease the burden especially on radiologists. In this paper, we propose a novel approach using the long short-term memory (LSTM) based model and gated recurrent unit (GRU) model as a decoder in combination with five different convolutional neural network (CNN) models, in which the DenseNet169 model shows the most promising results. Our LSTM-based decoder with the DenseNet169 model achieves the highest results in terms of the BLEU score B1 at 0.5856, B2 at 0.4982, B3 at 0.3470, B4 at 0.1269, ROUGE at 0.4534, and CIDEr at 0.463. Although our GRU-based model achieves the results in terms of the BLEU score (B1: 0.5619, B2: 0.4682, B3: 0.3370, B4: 0.1169), ROUGE (0.4401), and CIDEr (0.4320), the empirical evaluations demonstrate that our proposed approach, especially when combined with DenseNet169, achieves more accurate disease identification and generates reports of fluent and accurate radiological findings. The approach also compared with existing baseline methods.<\/jats:p>","DOI":"10.1142\/s0218001425400051","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T06:02:59Z","timestamp":1752732179000},"source":"Crossref","is-referenced-by-count":1,"title":["TraHeaLRG: Transformative Healthcare Leveraging LSTM and GRU Models Toward Improved Accuracy for Chest X-Ray Report Generation"],"prefix":"10.1142","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5107-8264","authenticated-orcid":false,"given":"Wajahat","family":"Akbar","sequence":"first","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3427-7505","authenticated-orcid":false,"given":"Shuguang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4413-389X","authenticated-orcid":false,"given":"Abdullah","family":"Soomro","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Islamia University of Bahawalpur, Bahawalpur, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6839-5969","authenticated-orcid":false,"given":"Altaf","family":"Hussain","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9819-7945","authenticated-orcid":false,"given":"Razaz Waheeb","family":"Attar","sequence":"additional","affiliation":[{"name":"Department of Management, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O. 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