{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T05:40:35Z","timestamp":1736142035754,"version":"3.32.0"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The utilization of automated software tools is imperative to enhance the efficiency of lung diseases through the analysis of X-ray images. The main objective of this study is to employ an analysis of chest X-ray images to diagnose lung disease. This study presents an Optimized Convolutional Neural Network (CNNFPA) designed to automate the diagnosis of lung disease. The Flower pollination technique is employed to optimize the hyperparameters associated with the training of the layers of the Convolutional Neural Network (CNN). In this paper, a novel model called RCNNFPA model is proposed, which makes use of a pre-trained ResNet50 with its layers frozen. Subsequently, CNNFPA architecture is integrated on top of the frozen ResNet-50 layers. This approach allowed us to leverage the knowledge captured by the ResNet-50 model on a large-scale dataset. To assess the efficacy of the proposed model and perform a comparison study using several classification methodologies, various publicly available datasets comprising images of COVID-19, Viral Pneumonia, Normal, and Tuberculosis are employed. As optimized and elaborated upon in this study, the CNN model is juxtaposed with existing state-of-the-art models. The proposed novel RCNNFPA model demonstrates considerable potential in facilitating the automated screening of individuals affected by different lung diseases.<\/jats:p>","DOI":"10.1093\/comjnl\/bxae071","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T23:51:32Z","timestamp":1722297092000},"page":"3080-3093","source":"Crossref","is-referenced-by-count":2,"title":["Flower pollination-enhanced CNN for lung disease diagnosis"],"prefix":"10.1093","volume":"67","author":[{"given":"Kevisino","family":"Khate","sequence":"first","affiliation":[{"name":"Computer Science and Engineering , National Institute of Technology Nagaland, Chumukedima, Dimapur, 797103 Nagaland,","place":["India"]}]},{"given":"Bam","family":"Bahadur Sinha","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering , National Institute of Technology Sikkim, 737139 Sikkim,","place":["India"]}]},{"given":"Arambam","family":"Neelima","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering , National Institute of Technology Nagaland, Chumukedima, Dimapur, 797103 Nagaland,","place":["India"]}]}],"member":"286","published-online":{"date-parts":[[2024,7,29]]},"reference":[{"key":"2025010523430000600_ref1","doi-asserted-by":"crossref","first-page":"241.e1","DOI":"10.1016\/j.anpedi.2020.02.001","article-title":"Recommendations on the clinical management of the COVID-19 infection by the new coronavirus SARS-CoV2","volume":"92","author":"Calvo","year":"2020","journal-title":"An Pediatr"},{"key":"2025010523430000600_ref2","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1056\/NEJMoa2001316","article-title":"Early transmission dynamics in Wuhan, China, of novel coronavirus\u2013infected pneumonia","volume":"382","author":"Li","year":"2020","journal-title":"N Engl J Med"},{"key":"2025010523430000600_ref3","doi-asserted-by":"crossref","first-page":"e26459","DOI":"10.2196\/26459","article-title":"Loss of smell and taste in patients with suspected COVID-19: analyses of patients\u2019 reports on social media","volume":"23","author":"Koyama","year":"2021","journal-title":"J Med Internet Res"},{"key":"2025010523430000600_ref4","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.ijid.2020.10.067","article-title":"Asymptomatic hypoxia in COVID-19 is associated with poor outcome","volume":"102","author":"Brouqui","year":"2021","journal-title":"Int J Infect Dis"},{"key":"2025010523430000600_ref5","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1148\/radiol.2020201365","article-title":"The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner society","volume":"296","author":"Rubin","year":"2020","journal-title":"Radiology"},{"key":"2025010523430000600_ref6","doi-asserted-by":"crossref","first-page":"110495","DOI":"10.1016\/j.chaos.2020.110495","article-title":"CoroDet: a deep learning based classification for COVID-19 detection using chest X-ray images","volume":"142","author":"Hussain","year":"2021","journal-title":"Chaos Soliton Fract"},{"key":"2025010523430000600_ref7","doi-asserted-by":"crossref","first-page":"105532","DOI":"10.1016\/j.cmpb.2020.105532","article-title":"COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios","volume":"194","author":"Pereira","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"2025010523430000600_ref8","doi-asserted-by":"crossref","first-page":"109944","DOI":"10.1016\/j.chaos.2020.109944","article-title":"Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet","volume":"138","author":"Panwar","year":"2020","journal-title":"Chaos Soliton Fract"},{"key":"2025010523430000600_ref9","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1016\/j.patrec.2020.09.010","article-title":"COVID-CAPS: a capsule network-based framework for identification of Covid-19 cases from X-ray images","volume":"138","author":"Afshar","year":"2020","journal-title":"Pattern Recogn Lett"},{"key":"2025010523430000600_ref10","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1007\/s42600-023-00302-x","article-title":"Classification of suspected objects and severity assessment of COVID-19 from chest X-ray images using deep transfer learning","volume":"39","author":"Verma","year":"2023","journal-title":"Res Biomed Eng"},{"year":"2020","author":"Hemdan","key":"2025010523430000600_ref11"},{"key":"2025010523430000600_ref12","doi-asserted-by":"crossref","first-page":"110170","DOI":"10.1016\/j.chaos.2020.110170","article-title":"Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches","volume":"140","author":"Hassantabar","year":"2020","journal-title":"Chaos Soliton Fract"},{"key":"2025010523430000600_ref13","doi-asserted-by":"crossref","first-page":"13855","DOI":"10.1007\/s11042-022-13843-7","article-title":"CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung cancer, and tuberculosis using chest X-rays","volume":"82","author":"Malik","year":"2023","journal-title":"Multimed Tools Appl"},{"key":"2025010523430000600_ref14","doi-asserted-by":"crossref","first-page":"14473","DOI":"10.1007\/s00521-021-06102-7","article-title":"Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework","volume":"35","author":"Yi","year":"2023","journal-title":"Neural Comput Appl"},{"key":"2025010523430000600_ref15","doi-asserted-by":"crossref","first-page":"14219","DOI":"10.1007\/s11042-022-13826-8","article-title":"A COVID-19 X-ray image classification model based on an enhanced convolutional neural network and hill climbing algorithms","volume":"82","author":"Pradhan","year":"2023","journal-title":"Multimed Tools Appl"},{"key":"2025010523430000600_ref16","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1016\/j.bbe.2022.06.005","article-title":"Automated diagnosis of COVID stages from lung CT images using statistical features in 2-dimensional flexible analytic wavelet transform","volume":"42","author":"Patel","year":"2022","journal-title":"Biocybern Biomed Eng"},{"key":"2025010523430000600_ref17","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1002\/ima.22865","article-title":"Automated diagnosis of COVID stages using texture-based Gabor features in variational mode decomposition from CT images","volume":"33","author":"Patel","year":"2023","journal-title":"Int J Imaging Syst Tech"},{"key":"2025010523430000600_ref18","doi-asserted-by":"crossref","first-page":"1762","DOI":"10.1080\/21681163.2023.2187244","article-title":"Machine learning-based lung disease diagnosis from CT images using Gabor features in Littlewood Paley empirical wavelet transform (LPEWT) and LLE","volume":"11","author":"Patel","year":"2023","journal-title":"Comput. methods Biomech Biomed Eng Imaging Vis"},{"key":"2025010523430000600_ref19","doi-asserted-by":"crossref","first-page":"107125","DOI":"10.1016\/j.knosys.2021.107125","article-title":"A species-based Flower Pollination Algorithm with increased selection pressure in abiotic local pollination and enhanced intensification","volume":"225","author":"Ozsoydan","year":"2021","journal-title":"Knowl Based Syst"},{"key":"2025010523430000600_ref20","doi-asserted-by":"crossref","first-page":"115496","DOI":"10.1016\/j.eswa.2021.115496","article-title":"Chaos and intensification enhanced Flower Pollination Algorithm to solve mechanical design and unconstrained function optimization problems","volume":"184","author":"Ozsoydan","year":"2021","journal-title":"Expert Syst Appl"},{"key":"2025010523430000600_ref21","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/978-3-319-67669-2_5","article-title":"Variants of the Flower Pollination Algorithm: a review","volume":"744","author":"Alyasseri","year":"2018","journal-title":"Nature-Inspired Algorithms and Applied Optimization"},{"key":"2025010523430000600_ref22","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1093\/comjnl\/bxac136","article-title":"Lung lobe segmentation and feature extraction-based hierarchical attention network for COVID-19 prediction from chest X-ray images","volume":"66","author":"Christina Magneta","year":"2023","journal-title":"Comput J"},{"key":"2025010523430000600_ref23","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1093\/comjnl\/bxac015","article-title":"A novel approach for CT-based COVID-19 classification and lesion segmentation based on deep learning","volume":"66","author":"Truong","year":"2023","journal-title":"Comput J"},{"key":"2025010523430000600_ref24","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1093\/comjnl\/bxac154","article-title":"Analysis performance of image processing technique its application by decision support systems on Covid-19 disease prediction using convolution neural network","volume":"66","author":"Ravishankar","year":"2023","journal-title":"Comput J"},{"key":"2025010523430000600_ref25","first-page":"770","article-title":"Deep residual learning for image recognition","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27\u201330 June","author":"He","year":"2016"},{"key":"2025010523430000600_ref26","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1007\/978-3-642-32894-7_27","article-title":"Flower pollination algorithm for global optimization","volume-title":"International conference on unconventional computing and natural computation, Orl\u00e9ans, France, 3\u20137 September","author":"Yang","year":"2012"},{"key":"2025010523430000600_ref27","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1007\/s00158-019-02380-x","article-title":"Optimum design of reinforced concrete retaining walls with the Flower Pollination Algorithm","volume":"61","author":"Mergos","year":"2020","journal-title":"Struct Multidiscip Optim"},{"key":"2025010523430000600_ref28","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1080\/0305215X.2013.832237","article-title":"Flower pollination algorithm: a novel approach for multiobjective optimization","volume":"46","author":"Yang","year":"2014","journal-title":"Eng Optim"},{"key":"2025010523430000600_ref29","doi-asserted-by":"crossref","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","article-title":"Can AI help in screening viral and COVID-19 pneumonia?","volume":"8","author":"Chowdhury","year":"2020","journal-title":"IEEE Access"},{"key":"2025010523430000600_ref30","doi-asserted-by":"crossref","first-page":"104319","DOI":"10.1016\/j.compbiomed.2021.104319","article-title":"Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images","volume":"132","author":"Rahman","year":"2021","journal-title":"Comput Biol Med"},{"key":"2025010523430000600_ref31","doi-asserted-by":"crossref","first-page":"191586","DOI":"10.1109\/ACCESS.2020.3031384","article-title":"Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization","volume":"8","author":"Rahman","year":"2020","journal-title":"IEEE Access"},{"key":"2025010523430000600_ref32","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.105002","article-title":"COVID-19 infection localization and severity grading from chest X-ray images","volume":"139","author":"Tahir","year":"2021","journal-title":"Comput Biol Med"},{"key":"2025010523430000600_ref33","doi-asserted-by":"crossref","first-page":"132","DOI":"10.36548\/jismac.2021.2.006","article-title":"Design of accurate classification of COVID-19 disease in X-ray images using deep learning approach","volume":"2","author":"Chen","year":"2021","journal-title":"J ISMAC"},{"key":"2025010523430000600_ref34","doi-asserted-by":"crossref","first-page":"110120","DOI":"10.1016\/j.chaos.2020.110120","article-title":"Comparison of deep learning approaches to predict COVID-19 infection","volume":"140","author":"Alakus","year":"2020","journal-title":"Chaos Soliton Fract"},{"key":"2025010523430000600_ref35","doi-asserted-by":"crossref","first-page":"109041","DOI":"10.1016\/j.ejrad.2020.109041","article-title":"Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study","volume":"128","author":"Wu","year":"2020","journal-title":"Eur J Radiol"}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/67\/11\/3080\/58677791\/bxae071.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/67\/11\/3080\/58677791\/bxae071.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T04:31:15Z","timestamp":1736137875000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/article\/67\/11\/3080\/7723329"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,29]]},"references-count":35,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,7,29]]},"published-print":{"date-parts":[[2024,11,20]]}},"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxae071","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"type":"print","value":"0010-4620"},{"type":"electronic","value":"1460-2067"}],"subject":[],"published-other":{"date-parts":[[2024,11]]},"published":{"date-parts":[[2024,7,29]]}}}