{ "ok": true, "doi": "10.46243/jst.2021.v6.i05.pp153-158", "doi_display": "10.46243/jst.2021.v6.i05.pp153-158", "doi_url": "https://doi.org/10.46243/jst.2021.v6.i05.pp153-158", "state": "registered", "url": "https://www.jst.org.in/index.php/pub/article/view/627", "title": "COMPARITIVE ANALYSIS OF LUNG DISEASE DETECTION USING DEEP LEARNING MODELS", "version": 2, "registered_via": "crossref", "prefix": { "prefix": "10.46243", "status": "live" }, "registrant": { "name": "Longman Publishers", "kind": "publisher", "country": "India" }, "reserved_at": null, "registered_at": "2026-09-29 22:00:41", "updated_at": "2026-09-30 00:00:01", "withdrawn_at": null, "withdrawn_reason": null, "record": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2021.v6.i05.pp153-158", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "COMPARITIVE ANALYSIS OF LUNG DISEASE DETECTION USING DEEP LEARNING MODELS", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2021.v6.i05.pp153-158" } ], "agents": [ { "role": "author", "name": { "given": "Dr.Syed", "family": "Abdul Sattar" }, "sequence": "first" }, { "role": "author", "name": { "given": "d Khaleel", "family": "Ahmed" }, "sequence": "additional" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2021-09-01", "date_type": "PublicationDate", "online": "2021-09-01" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "06", "issue": "05", "pages": { "first": "153", "last": "158" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/627", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/627/564", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/627/3794", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://www.jst.org.in/index.php/pub/article/view/627/564", "purpose": "similarity-checking" } ], "abstract": { "value": "Now a days for identifying or predict any diseases on human beings, we should have proper diagnosis for predicting the disease which is present in that human body. In general for prediction of diseases we try to use either X-Ray, CT or MRI scan techniques for taking decision on that appropriate disease. In general medical person need complete knowledge on that appropriate domain to find out the abnormality which is present in human beings. As we all know that India tops the world for having more deaths due to lung diseases. After the second highest cause of deaths in India due to heart disease, this ling disease is one which is increasing its rank more and more. In order to reduce that problem early diagnosis and treatment of lung diseases is critical to prevent complications including death. Normally for finding the abnormality present in lung, chest X-ray is playing very important role to detect the complete information about the lungs. In this current article we try to present an effective way for expert diagnosis of lung diseases using deep learning models. It focuses on creating a system for assistance of Radiologists in detection of lung diseases. This will especially benefit rural areas where radiologists aren’t easily available. We use two models like Vgg16 and Vgg19 for predicting the lung disease from chest X ray images and then tell which model gives high accuracy and performance. We conclude by discussing research obstacles, emerging trends, and possible future directions for improving some more advancement.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2021-09-01", "applies_to": "vor" }, "references": [ { "key": "ref1", "unstructured": "Bousquet, J. Global Surveillance, Prevention and Control of Chronic Respiratory Diseases; World Health Organization: Geneva, Switzerland, 2007; pp. 12–36" }, { "key": "ref2", "unstructured": "Forum of International Respiratory Societies. The Global Impact of Respiratory Disease, 2nd ed.; European Respiratory Society, Sheffield, UK, 2017; pp. 5–42. © 2021 JETIR May 2021, Volume 8, Issue 5 www.jetir.org (ISSN-2349-5162) JETIR2105898 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org g759" }, { "key": "ref3", "unstructured": "World Health Organization. Corona virus Disease 2019 (COVID-19) Situation Report; Technical Report March; World Health Organization: Geneva, Switzerland, 2020" }, { "key": "ref4", "doi": "10.3233/xst-200715", "unstructured": "Rahaman, M.M.; Li, C.; Yao, Y.; Kulwa, F.; Rahman, M.A.; Wang, Q.; Qi, S.; Kong, F.; Zhu, X.; Zhao, X. Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches. J. X-Ray Sci. Technol. 2020, 28, 821–839" }, { "key": "ref5", "unstructured": "Yahiaoui, A.; Er, O.; Yumusak, N. A new method of automatic recognition for tuberculosis disease diagnosis using support vector machines. Biomed. Res. 2017, 28, 4208–4212" }, { "key": "ref6", "doi": "10.1016/j.patcog.2018.05.014", "unstructured": "Hu, Z.; Tang, J.; Wang, Z.; Zhang, K.; Zhang, L.; Sun, Q. Deep learning for image-based cancer detection and diagnosis-A survey. Pattern Recognit. 2018, 83, 134–149" }, { "key": "ref7", "doi": "10.1164/ajrccm.161.4.16141", "unstructured": "American Thoracic Society. Diagnostic Standards and Classification of Tuberculosis in Adults and Children. Am. J. Respir. Crit. Care Med. 2000, 161, 1376–1395" }, { "key": "ref8", "doi": "10.1016/j.media.2017.06.015", "unstructured": "Setio, A.A.A.; Traverso, A.; de Bel, T.; Berens, M.S.; van den Bogaard, C.; Cerello, P.; Chen, H.; Dou, Q.; Fantacci, M.E.; Geurts, B.; et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med. Image Anal. 2017, 42, 1–13" }, { "key": "ref9", "doi": "10.1146/annurev-bioeng-071516-044442", "unstructured": "Shen, D.; Wu, G.; Suk, H.I. Deep Learning in Medical Image Analysis. Annu. Rev. Biomed. Eng. 2017, 19, 221–248" }, { "key": "ref10", "doi": "10.1109/access.2018.2823979", "unstructured": "Wu, C.; Luo, C.; Xiong, N.; Zhang, W.; Kim, T.H. A Greedy Deep Learning Method for Medical Disease Analysis. IEEE Access 2018, 6, 20021–20030" }, { "key": "ref11", "doi": "10.1007/s11684-019-0726-4", "unstructured": "Ma, J.; Song, Y.; Tian, X.; Hua, Y.; Zhang, R.; Wu, J. Survey on deep learning for pulmonary medical imaging. Front. Med. 2019, 14, 450–469" }, { "key": "ref12", "doi": "10.1109/embc.2018.8512337", "unstructured": "Rajaraman, S.; Candemir, S.; Xue, Z.; Alderson, P.O.; Kohli, M.; Abuya, J.; Thoma, G.R.; Antani, S.; Member, S. A novel stacked generalization of models for improved TB detection in chest radiographs. In Proceedings of the 2018 40th Annual International Conference the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 17–21 July 2018; pp. 718–721" } ], "record": { "registrant": "Longman Publishers", "registered": "2026-09-18", "updated": "2026-09-21", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "record_sha256": "5649b4102755bab5811ddecd0a39c336c9b60a2cf0a477a4dc9792aca1af4c2b", "handle": { "synced_at": "2026-10-01 18:14:01", "url": "https://www.jst.org.in/index.php/pub/article/view/627" }, "links": { "record_page": "https://registry.smartscholars.in/record.php?doi=10.46243%2Fjst.2021.v6.i05.pp153-158", "system_metadata": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2021.v6.i05.pp153-158&as=system", "history": "https://registry.smartscholars.in/api.php?action=history&doi=10.46243%2Fjst.2021.v6.i05.pp153-158", "kernel_xml": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2021.v6.i05.pp153-158&as=xml" } }