{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:06:03Z","timestamp":1770271563222,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,12,10]],"date-time":"2023-12-10T00:00:00Z","timestamp":1702166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Erasmus+","award":["GA 101083048"],"award-info":[{"award-number":["GA 101083048"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In this work, we propose a CAD (computer-aided diagnosis) system using advanced deep-learning models and computer vision techniques that can improve diagnostic accuracy and reduce transmission risks using the YOLOv7 (You Only Look Once, version 7) object detection architecture. The proposed system is capable of accurate object detection, which provides a bounding box denoting the area in the X-rays that shows some possibility of TB (tuberculosis). The system makes use of CNNs (Convolutional Neural Networks) and YOLO models for the detection of the consolidation of cavitary patterns of the lesions and their detection, respectively. For this study, we experimented on the TBX11K dataset, which is a publicly available dataset. In our experiment, we employed class weights and data augmentation techniques to address the data imbalance present in the dataset. This technique shows a promising improvement in the model\u2019s performance and thus better generalization. In addition, it also shows that the developed model achieved promising results with a mAP (mean average precision) of 0.587, addressing class imbalance and yielding a robust performance for both obsolete pulmonary TB and active TB detection. Thus, our CAD system, rooted in state-of-the-art deep-learning and computer vision methodologies, not only advances diagnostic accuracy but also contributes to the mitigation of TB transmission risks. The substantial improvement in the model\u2019s performance and the ability to handle class imbalance underscore the potential of our approach for real-world TB detection applications.<\/jats:p>","DOI":"10.3390\/info14120655","type":"journal-article","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T13:18:21Z","timestamp":1702300701000},"page":"655","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Advancing Tuberculosis Detection in Chest X-rays: A YOLOv7-Based Approach"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0638-5840","authenticated-orcid":false,"given":"Rabindra","family":"Bista","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Kathmandu University, Dhulikhel 45200, Nepal"}]},{"given":"Anurag","family":"Timilsina","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kathmandu University, Dhulikhel 45200, Nepal"}]},{"given":"Anish","family":"Manandhar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kathmandu University, Dhulikhel 45200, Nepal"}]},{"given":"Ayush","family":"Paudel","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kathmandu University, Dhulikhel 45200, Nepal"}]},{"given":"Avaya","family":"Bajracharya","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kathmandu University, Dhulikhel 45200, Nepal"}]},{"given":"Sagar","family":"Wagle","sequence":"additional","affiliation":[{"name":"Department of Rad\/Musculoskeletal Imaging, Stanford University, Stanford, CA 94305, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6662-0806","authenticated-orcid":false,"given":"Joao C.","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Inov Inesc Inova\u00e7\u00e3o\u2014Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal"},{"name":"Instituto Universit\u00e1rio de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,10]]},"reference":[{"key":"ref_1","unstructured":"(2023, August 01). 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