{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T16:30:02Z","timestamp":1792600202989,"version":"4.5.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:00:00Z","timestamp":1657756800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:00:00Z","timestamp":1657756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s11517-022-02632-x","type":"journal-article","created":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T08:04:56Z","timestamp":1657785896000},"page":"2681-2691","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques"],"prefix":"10.1007","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2331-4507","authenticated-orcid":false,"given":"Lokeswari","family":"Venkataramana","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D. Venkata Vara","family":"Prasad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Saraswathi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C. M.","family":"Mithumary","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Karthikeyan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Monika","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,14]]},"reference":[{"key":"2632_CR1","doi-asserted-by":"crossref","unstructured":"Kumar P, Bhatnagar R, Gaur K, Bhatnagar A (2021) Classification of imbalanced data: review of methods and applications. In IOP conference series: materials science and engineering, vol 1099, No. 1. IOP Publishing, p 012077","DOI":"10.1088\/1757-899X\/1099\/1\/012077"},{"issue":"9","key":"2632_CR2","doi-asserted-by":"publisher","first-page":"1707","DOI":"10.1080\/01431169508954507","volume":"16","author":"GM Foody","year":"1995","unstructured":"Foody GM, McCulloch MB, Yates WB (1995) The effect of training set size and composition on artificial neural network classification. Int J Remote Sens 16(9):1707\u20131723","journal-title":"Int J Remote Sens"},{"issue":"4","key":"2632_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00695-5","volume":"2","author":"A Shelke","year":"2021","unstructured":"Shelke A, Inamdar M, Shah V, Tiwari A, Hussain A, Chafekar T, Mehendale N (2021) Chest X-ray classification using deep learning for automated COVID-19 screening. SN Comput Sci 2(4):1\u20139","journal-title":"SN Comput Sci"},{"issue":"9","key":"2632_CR4","doi-asserted-by":"publisher","first-page":"3233","DOI":"10.3390\/app10093233","volume":"10","author":"T Rahman","year":"2020","unstructured":"Rahman T, Chowdhury ME, Khandakar A, Islam KR, Islam KF, Mahbub ZB et al (2020) Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray. Appl Sci 10(9):3233","journal-title":"Appl Sci"},{"issue":"5","key":"2632_CR5","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122\u20131131","journal-title":"Cell"},{"key":"2632_CR6","doi-asserted-by":"crossref","unstructured":"Qjidaa M, Mechbal Y, Ben-Fares A, Amakdouf H, Maaroufi M, Alami B, Qjidaa H (2020) Early detection of COVID19 by deep learning transfer model for populations in isolated rural areas. In 2020 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE,\u00a0pp 1\u20135","DOI":"10.1109\/ISCV49265.2020.9204099"},{"issue":"10","key":"2632_CR7","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.3390\/app8101715","volume":"8","author":"S Rajaraman","year":"2018","unstructured":"Rajaraman S, Candemir S, Kim I, Thoma G, Antani S (2018) Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Appl Sci 8(10):1715","journal-title":"Appl Sci"},{"issue":"6","key":"2632_CR8","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1111\/j.1440-1843.2006.00947.x","volume":"11","author":"CK Liam","year":"2006","unstructured":"Liam CK, Pang YK, Poosparajah S (2006) Pulmonary tuberculosis presenting as community-acquired pneumonia. Respirology 11(6):786\u2013792","journal-title":"Respirology"},{"issue":"3","key":"2632_CR9","first-page":"259","volume":"6","author":"Y Bar","year":"2018","unstructured":"Bar Y, Diamant I, Wolf L, Lieberman S, Konen E, Greenspan H (2018) Chest pathology identification using deep feature selection with non-medical training. Comput Methods Biomech Biomed Engin: Imaging Visualization 6(3):259\u2013263","journal-title":"Comput Methods Biomech Biomed Engin: Imaging Visualization"},{"issue":"12","key":"2632_CR10","first-page":"310","volume":"6","author":"S Sharma","year":"2017","unstructured":"Sharma S, Sharma S, Athaiya A (2017) Activation functions in neural networks. Towards Data Sci 6(12):310\u2013316","journal-title":"Towards Data Sci"},{"key":"2632_CR11","unstructured":"Barhoom, Alaa MA (2019) Pneumonia diagnosis using deep learning, PhD dissertation., Al-Azhar University-Gaza"},{"issue":"1","key":"2632_CR12","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s40747-020-00199-4","volume":"7","author":"ME Karar","year":"2021","unstructured":"Karar ME, Hemdan EED, Shouman MA (2021) Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans. Complex Intell Syst 7(1):235\u2013247","journal-title":"Complex Intell Syst"},{"key":"2632_CR13","doi-asserted-by":"crossref","unstructured":"Ozsoz M, Ibrahim AU, Serte S, Al-Turjman F, Yakoi PS (2020) Viral and bacterial pneumonia detection using artificial intelligence in the era of COVID-19","DOI":"10.21203\/rs.3.rs-70158\/v1"},{"issue":"4","key":"2632_CR14","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1016\/j.bbe.2020.08.008","volume":"40","author":"G Jain","year":"2020","unstructured":"Jain G, Mittal D, Thakur D, Mittal MK (2020) A deep learning approach to detect Covid-19 coronavirus with X-ray images. Biocybern Biomed Eng 40(4):1391\u20131405","journal-title":"Biocybern Biomed Eng"},{"key":"2632_CR15","unstructured":"Chest X-ray images (pneumonia).\u00a0https:\/\/www.kaggle.com\/paultimothymooney\/chest-xray-pneumonia.\u00a0Accessed 12 July 2022"},{"key":"2632_CR16","unstructured":"Tuberculosis (TB) chest X-ray database.\u00a0https:\/\/www.kaggle.com\/paultimothymooney\/chest-xray-pneumonia. Accessed 12 July 2022."},{"key":"2632_CR17","unstructured":"Curated chest X-ray image dataset for COVID-19. https:\/\/www.kaggle.com\/unaissait\/curated-chest-xray-image-dataset-for-covid19. Accessed 12 July 2022"},{"key":"2632_CR18","unstructured":"Perez L, Wang J (2017) The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621"},{"key":"2632_CR19","doi-asserted-by":"crossref","unstructured":"Miko\u0142ajczyk A, Grochowski M (2018) Data augmentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD workshop (IIPhDW). IEEE,\u00a0pp 117\u2013122","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"2632_CR20","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"2632_CR21","doi-asserted-by":"crossref","unstructured":"Wang Q, Luo Z, Huang J, Feng Y, Liu Z (2017) A novel ensemble method for imbalanced data learning: bagging of extrapolation-SMOTE SVM.\u00a0Comput Intell Neurosci\u00a02017","DOI":"10.1155\/2017\/1827016"},{"issue":"1","key":"2632_CR22","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1504\/IJKESDP.2011.039875","volume":"3","author":"HM Nguyen","year":"2011","unstructured":"Nguyen HM, Cooper EW, Kamei K (2011) Borderline over-sampling for imbalanced data classification. J Knowl Eng Soft Data Paradig 3(1):4\u201321","journal-title":"J Knowl Eng Soft Data Paradig"},{"key":"2632_CR23","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1613\/jair.1.11192","volume":"61","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez A, Garcia S, Herrera F, Chawla NV (2018) SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J Artif Intell Res 61:863\u2013905","journal-title":"J Artif Intell Res"},{"key":"2632_CR24","first-page":"152","volume-title":"International Conference on Neural Information Processing","author":"P Jeatrakul","year":"2010","unstructured":"Jeatrakul P, Wong KW, Fung CC (2010) Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm. International Conference on Neural Information Processing. Springer, Berlin, Heidelberg, pp 152\u2013159"},{"key":"2632_CR25","doi-asserted-by":"publisher","first-page":"114986","DOI":"10.1016\/j.eswa.2021.114986","volume":"178","author":"A \u00d6zdemir","year":"2021","unstructured":"\u00d6zdemir A, Polat K, Alhudhaif A (2021) Classification of imbalanced hyperspectral images using SMOTE-based deep learning methods. Expert Syst Appl 178:114986","journal-title":"Expert Syst Appl"},{"key":"2632_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107269","volume":"228","author":"Y Li","year":"2021","unstructured":"Li Y, Wang Y, Li T, Li B, Lan X (2021) SP-SMOTE: a novel space partitioning based synthetic minority oversampling technique. Knowl-Based Syst 228:107269","journal-title":"Knowl-Based Syst"},{"key":"2632_CR27","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1016\/j.ins.2021.03.041","volume":"565","author":"J Li","year":"2021","unstructured":"Li J, Zhu Q, Wu Q, Fan Z (2021) A novel oversampling technique for class-imbalanced learning based on SMOTE and natural neighbors. Inf Sci 565:438\u2013455","journal-title":"Inf Sci"},{"key":"2632_CR28","doi-asserted-by":"crossref","unstructured":"Waqar M, Dawood H, Dawood H, Majeed N, Banjar A, Alharbey R (2021) An efficient SMOTE-based deep learning model for heart attack prediction. Scientific Programming, 2021","DOI":"10.1155\/2021\/6621622"},{"key":"2632_CR29","first-page":"18","volume":"17","author":"R Nagamounika","year":"2020","unstructured":"Nagamounika R, Sri CNSV, Harshitha A, Tejaswi KL, Lakshmi PRSM (2020) Prediction of pneumonia disease by using deep convolutional neural networks. J Eng Sci Criterion 17:18","journal-title":"J Eng Sci Criterion"},{"issue":"8","key":"2632_CR30","doi-asserted-by":"publisher","first-page":"2688","DOI":"10.1109\/TMI.2020.2993291","volume":"39","author":"Y Oh","year":"2020","unstructured":"Oh Y, Park S, Ye JC (2020) Deep learning COVID-19 features on CXR using limited training data sets. IEEE Trans Med Imaging 39(8):2688\u20132700","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"2632_CR31","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1002\/ima.22613","volume":"31","author":"S Johri","year":"2021","unstructured":"Johri S, Goyal M, Jain S, Baranwal M, Kumar V, Upadhyay R (2021) A novel machine learning-based analytical framework for automatic detection of COVID-19 using chest X-ray images. Int J Imaging Syst Technol 31(3):1105\u20131119","journal-title":"Int J Imaging Syst Technol"},{"key":"2632_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105134","volume":"141","author":"JL Gayathri","year":"2022","unstructured":"Gayathri JL, Abraham B, Sujarani MS, Nair MS (2022) A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse autoencoder and feed forward neural network. Comput Biol Med 141:105134","journal-title":"Comput Biol Med"},{"key":"2632_CR33","doi-asserted-by":"crossref","unstructured":"Heidari A, Navimipour NJ, Unal M, Toumaj S (2021) The COVID-19 epidemic analysis and diagnosis using deep learning: a systematic literature review and future directions.\u00a0Comput Biol Med 105141","DOI":"10.1016\/j.compbiomed.2021.105141"},{"issue":"2","key":"2632_CR34","doi-asserted-by":"publisher","first-page":"559","DOI":"10.3390\/app10020559","volume":"10","author":"V Chouhan","year":"2020","unstructured":"Chouhan V, Singh SK, Khamparia A, Gupta D, Tiwari P, Moreira C, De Albuquerque VHC (2020) A novel transfer learning based approach for pneumonia detection in chest X-ray images. Applied Sciences 10(2):559","journal-title":"Applied Sciences"},{"key":"2632_CR35","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.patrec.2021.08.018","volume":"151","author":"RF Mansour","year":"2021","unstructured":"Mansour RF, Escorcia-Gutierrez J, Gamarra M, Gupta D, Castillo O, Kumar S (2021) Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification. Pattern Recogn Lett 151:267\u2013274","journal-title":"Pattern Recogn Lett"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02632-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02632-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02632-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T05:21:19Z","timestamp":1660108879000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02632-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,14]]},"references-count":35,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["2632"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02632-x","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,14]]},"assertion":[{"value":"31 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent is not necessary as this article does not involve human or animal participants.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}