{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T04:13:03Z","timestamp":1773029583882,"version":"3.50.1"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T00:00:00Z","timestamp":1687305600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T00:00:00Z","timestamp":1687305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11227-023-05452-4","type":"journal-article","created":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T14:02:16Z","timestamp":1687356136000},"page":"21449-21473","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automatic detection of COVID-19 and pneumonia from chest X-ray images using texture features"],"prefix":"10.1007","volume":"79","author":[{"given":"Farnaz","family":"Sheikhi","sequence":"first","affiliation":[]},{"given":"Aliakbar","family":"Taghdiri","sequence":"additional","affiliation":[]},{"given":"Danial","family":"Moradisabzevar","sequence":"additional","affiliation":[]},{"given":"Hanieh","family":"Rezakhani","sequence":"additional","affiliation":[]},{"given":"Hasti","family":"Daneshkia","sequence":"additional","affiliation":[]},{"given":"Mobina","family":"Goodarzi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,21]]},"reference":[{"key":"5452_CR1","unstructured":"https:\/\/symptomate.com\/"},{"key":"5452_CR2","unstructured":"https:\/\/www.skinvision.com\/"},{"key":"5452_CR3","unstructured":"https:\/\/ada.com\/app\/"},{"key":"5452_CR4","unstructured":"https:\/\/www.wolterskluwer.com\/en\/solutions\/uptodate"},{"key":"5452_CR5","unstructured":"https:\/\/www.kaggle.com\/paultimothymooney\/chest-xray-pneumonia"},{"key":"5452_CR6","unstructured":"https:\/\/github.com\/abzargar\/COVID-Classifier"},{"key":"5452_CR7","unstructured":"https:\/\/www.worldometers.info\/coronavirus\/, 2022. [Online; accessed 30-November-2022]"},{"key":"5452_CR8","unstructured":"https:\/\/www.who.int\/activities\/tracking-SARS-CoV-2-variants, 2022. [Online; accessed 30-November-2022]"},{"key":"5452_CR9","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1007\/s10489-020-01829-7","volume":"51","author":"A Abbas","year":"2021","unstructured":"Abbas A, Abdelsamea MM, Medhat Gaber M (2021) Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl Intell 51:854\u2013864","journal-title":"Appl Intell"},{"key":"5452_CR10","doi-asserted-by":"crossref","first-page":"110071","DOI":"10.1016\/j.chaos.2020.110071","volume":"140","author":"A Altan","year":"2020","unstructured":"Altan A, Karasu S (2020) Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique. Chaos Solitons Fractals 140:110071","journal-title":"Chaos Solitons Fractals"},{"key":"5452_CR11","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s13246-020-00865-4","volume":"43","author":"ID Apostolopoulos","year":"2020","unstructured":"Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43:635\u2013640","journal-title":"Phys Eng Sci Med"},{"key":"5452_CR12","doi-asserted-by":"crossref","first-page":"110861","DOI":"10.1016\/j.chaos.2021.110861","volume":"146","author":"K ArunKumar","year":"2021","unstructured":"ArunKumar K, Kalaga DV, Kumar CMS, Kawaji M, Brenza TM (2021) Forecasting of COVID-19 using deep layer recurrent neural networks (RNNs) with gated recurrent units (GRUs) and long short-term memory (LSTM) cells. Chaos Solitons Fractals 146:110861","journal-title":"Chaos Solitons Fractals"},{"key":"5452_CR13","doi-asserted-by":"crossref","first-page":"8052","DOI":"10.3390\/ijerph18158052","volume":"18","author":"PD Barua","year":"2021","unstructured":"Barua PD, Gowdh NFM, Rahmat K, Ramli N, Ng WL, Chan WY, Kuluozturk M, Dogan S, Baygin M, Yaman O, Tuncer T, Wen T, Cheong KH, Acharya UR (2021) Automatic COVID-19 detection using exemplar hybrid deep features with X-ray images. Int J Environ Res Public Health 18:8052","journal-title":"Int J Environ Res Public Health"},{"issue":"5","key":"5452_CR14","doi-asserted-by":"crossref","first-page":"3026","DOI":"10.1007\/s10489-020-01978-9","volume":"51","author":"M Chakraborty","year":"2021","unstructured":"Chakraborty M, Dhavale SV, Ingole J (2021) Corona-Nidaan: lightweight deep convolutional neural network for chest X-ray based COVID-19 infection detection. Appl Intell 51(5):3026\u20133043","journal-title":"Appl Intell"},{"key":"5452_CR15","doi-asserted-by":"crossref","first-page":"109864","DOI":"10.1016\/j.chaos.2020.109864","volume":"135","author":"VKR Chimmula","year":"2020","unstructured":"Chimmula VKR, Zhang L (2020) Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos Solitons Fractals 135:109864","journal-title":"Chaos Solitons Fractals"},{"key":"5452_CR16","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/4593330","author":"C Chola","year":"2022","unstructured":"Chola C, Benifa JVB, Guru DS, Muaad AY, Hanumanthappa J, Al-antari MA, AlSalman H, Gumaei AH (2022) Gender identification and classification of Drosophila melanogaster flies using machine learning techniques. Comput Math Methods Med. https:\/\/doi.org\/10.1155\/2022\/4593330","journal-title":"Comput Math Methods Med"},{"key":"5452_CR17","doi-asserted-by":"crossref","unstructured":"Chola C, Mallikarjuna P, Muaad AY, Bibal\u00a0Benifa JV, Hanumanthappa J, Al-antari MA (2022) A hybrid deep learning approach for COVID-19 diagnosis via CT and X-ray medical images. In: Computer Sciences and Mathematics Forum, vol 2, No 1","DOI":"10.3390\/IOCA2021-10909"},{"key":"5452_CR18","doi-asserted-by":"crossref","unstructured":"Cohen JP, Morrison P, Dao L, Roth K, Duong TQ, Ghassemi M (2020) COVID-19 image data collection: Prospective predictions are the future. arXiv 2006.11988","DOI":"10.59275\/j.melba.2020-48g7"},{"issue":"3","key":"5452_CR19","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.3390\/ijerph20032035","volume":"20","author":"M Constantinou","year":"2023","unstructured":"Constantinou M, Exarchos T, Vrahatis AG, Vlamos P (2023) COVID-19 classification on chest X-ray images using deep learning methods. Int J Environ Res Public Health 20(3):2035","journal-title":"Int J Environ Res Public Health"},{"issue":"6538","key":"5452_CR20","doi-asserted-by":"crossref","first-page":"eabg3055","DOI":"10.1126\/science.abg3055","volume":"327","author":"N Davies","year":"2021","unstructured":"Davies N, Abbott S, Barnard R, Jarvis C, Kucharski A, Munday J, Pearson C et al (2021) Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England. Science 327(6538):eabg3055","journal-title":"Science"},{"key":"5452_CR21","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929v2"},{"key":"5452_CR22","doi-asserted-by":"crossref","first-page":"115519","DOI":"10.1016\/j.eswa.2021.115519","volume":"184","author":"LT Duong","year":"2021","unstructured":"Duong LT, Le NH, Tran TB, Ngo VM, Nguyen PT (2021) Detection of tuberculosis from chest X-ray images: boosting the performance with vision transformer and transfer learning. Expert Syst Appl 184:115519","journal-title":"Expert Syst Appl"},{"key":"5452_CR23","doi-asserted-by":"crossref","unstructured":"Duong LT, Nguyen PT, Iovino L, Flammini M (2020) Deep learning for automated recognition of Covid-19 from chest X-ray images. medRxiv","DOI":"10.1101\/2020.08.13.20173997"},{"key":"5452_CR24","doi-asserted-by":"crossref","first-page":"109851","DOI":"10.1016\/j.asoc.2022.109851","volume":"132","author":"LT Duong","year":"2023","unstructured":"Duong LT, Nguyen PT, Iovino L, Flammini M (2023) Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning. Appl Soft Comp 132:109851","journal-title":"Appl Soft Comp"},{"key":"5452_CR25","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s00216-020-02889-x","volume":"413","author":"B Giri","year":"2021","unstructured":"Giri B, Pandey S, Shrestha R, Pokharel K, Ligler FS, Neupane BB (2021) Review of analytical performance of COVID-19 detection methods. Anal Bioanal Chem 413:35\u201348","journal-title":"Anal Bioanal Chem"},{"key":"5452_CR26","unstructured":"Han K, Xiao A, Wu E, Guo J, Xu C, Wang Y (2021) Transformer in transformer. arXiv:2103.00112v3"},{"issue":"5","key":"5452_CR27","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/PROC.1979.11328","volume":"67","author":"R Haralick","year":"1979","unstructured":"Haralick R (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786\u2013804","journal-title":"Proc IEEE"},{"key":"5452_CR28","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"6","author":"RM Haralick","year":"1973","unstructured":"Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610\u2013621","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"5452_CR29","doi-asserted-by":"crossref","first-page":"105045","DOI":"10.1016\/j.rinp.2021.105045","volume":"31","author":"JN Hasoon","year":"2021","unstructured":"Hasoon JN, Fadel AH, Hameed RS, Mostafa SA, Khalaf BA, Mohammed MA, Nedoma J (2021) COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images. Results Phys 31:105045","journal-title":"Results Phys"},{"key":"5452_CR30","unstructured":"Hemdan EE-D, Shouman MA, Karar ME (2020) COVIDX-Net: a framework of deep learning classifiers to diagnose COVID-19 in X-ray images. arXiv:2003.11055"},{"key":"5452_CR31","unstructured":"Jiang J, Lin S (2022) COVID-19 detection in chest X-ray images using swin-transformer and transformer in transformer. ArXiv:2110.08427v2"},{"issue":"4","key":"5452_CR32","doi-asserted-by":"crossref","first-page":"2000597","DOI":"10.1183\/13993003.00597-2020","volume":"55","author":"W Jie Guan","year":"2020","unstructured":"Jie Guan W, Chang Chen R, Shan Zhong N (2020) Strategies for the prevention and management of coronavirus disease. Eur Respir J 55(4):2000597","journal-title":"Eur Respir J"},{"key":"5452_CR33","doi-asserted-by":"crossref","first-page":"104252","DOI":"10.1016\/j.compbiomed.2021.104252","volume":"131","author":"W Jin","year":"2021","unstructured":"Jin W, Dong S, Dong C, Ye X (2021) A hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph. Comput Biol Med 131:104252","journal-title":"Comput Biol Med"},{"issue":"5","key":"5452_CR34","doi-asserted-by":"crossref","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, McKeown A, Yang G, Wu X, Yan F, Dong J, Prasadha MK, Pei J, Ting MY, Zhu J, Li C, Hewett S, Dong J, Ziyar I, Shi A, Zhang R, Zheng L, Hou R, Shi W, Fu X, Duan Y, Huu VA, Wen C, Zhang ED, Zhang CL, Li O, Wang X, Singer MA, Sun X, Xu J, Tafreshi A, Lewis MA, Xia H, Zhang K (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122-1131.e9","journal-title":"Cell"},{"key":"5452_CR35","doi-asserted-by":"crossref","unstructured":"Khan A, Khan SH, Saif M, Batool A, Sohail A, Khan MW (2022) A survey of deep learning techniques for the analysis of COVID-19 and their usability for detecting Omicron. arXiv:2202.06372","DOI":"10.1080\/0952813X.2023.2165724"},{"key":"5452_CR36","doi-asserted-by":"crossref","first-page":"9887","DOI":"10.1038\/s41598-021-88807-2","volume":"11","author":"AZ Khuzani","year":"2021","unstructured":"Khuzani AZ, Heidari M, Shariati SA (2021) COVID-classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images. Sci Rep 11:9887","journal-title":"Sci Rep"},{"issue":"8","key":"5452_CR37","doi-asserted-by":"crossref","first-page":"1880","DOI":"10.3390\/diagnostics12081880","volume":"12","author":"S Laddha","year":"2022","unstructured":"Laddha S, Mnasri S, Alghamdi M, Kumar V, Kaur M, Alrashidi M, Almuhaimeed A, Alshehri A, Alrowaily MA, Alkhazi I (2022) COVID-19 diagnosis and classification using radiological imaging and deep learning techniques: a comparative study. Diagnostics 12(8):1880","journal-title":"Diagnostics"},{"issue":"2","key":"5452_CR38","doi-asserted-by":"crossref","first-page":"taaa021","DOI":"10.1093\/jtm\/taaa021","volume":"27","author":"Y Liu","year":"2020","unstructured":"Liu Y, Gayle AA, Wilder-Smith A, Rockl\u00f6v J (2020) The reproductive number of COVID-19 is higher compared to SARS coronavirus. J Travel Med 27(2):taaa021","journal-title":"J Travel Med"},{"key":"5452_CR39","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. arXiv:2103.14030v2","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"4","key":"5452_CR40","doi-asserted-by":"crossref","first-page":"651","DOI":"10.3390\/sym12040651","volume":"12","author":"M Loey","year":"2020","unstructured":"Loey M, Smarandache F, Khalifa NEM (2020) Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on GAN and deep transfer learning. Symmetry 12(4):651","journal-title":"Symmetry"},{"issue":"4","key":"5452_CR41","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1007\/s42979-022-01184-z","volume":"3","author":"Y Meraihi","year":"2022","unstructured":"Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A, Alsaadi FE (2022) Machine learning-based research for COVID-19 detection, diagnosis, and prediction: a survey. SN Comput Sci 3(4):286","journal-title":"SN Comput Sci"},{"key":"5452_CR42","doi-asserted-by":"crossref","unstructured":"Mujahid M, Rustam F, \u00c1lvarez R, Luis Vidal Maz\u00f3n J, D\u00edez IDLT, I. Ashraf, (2022) Pneumonia classification from X-ray images with Inception-v3 and convolutional neural network. Diagnostics 12(5):1280","DOI":"10.3390\/diagnostics12051280"},{"issue":"8","key":"5452_CR43","doi-asserted-by":"crossref","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 Imag 39(8):2688\u20132700","journal-title":"IEEE Trans Med Imag"},{"key":"5452_CR44","doi-asserted-by":"crossref","unstructured":"Ojala T, Pietikainen M, Harwood D (1994) Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In Proceedings of 12th International Conference on Pattern Recognition, vol\u00a01, pp 582\u2013585","DOI":"10.1109\/ICPR.1994.576366"},{"key":"5452_CR45","doi-asserted-by":"crossref","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"121","author":"T Ozturk","year":"2020","unstructured":"Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 121:103792","journal-title":"Comput Biol Med"},{"key":"5452_CR46","doi-asserted-by":"crossref","first-page":"102299","DOI":"10.1016\/j.media.2021.102299","volume":"75","author":"S Park","year":"2022","unstructured":"Park S, Kim G, Oh Y, Seo JB, Lee SM, Kim JH, Moon S, Lim J-K, Ye JC (2022) Multi-task vision transformer using low-level chest X-ray feature corpus for COVID-19 diagnosis and severity quantification. Med Image Anals 75:102299","journal-title":"Med Image Anals"},{"issue":"2","key":"5452_CR47","doi-asserted-by":"crossref","first-page":"e13706","DOI":"10.1111\/eci.13706","volume":"52","author":"V Pecoraro","year":"2022","unstructured":"Pecoraro V, Negro A, Pirotti T, Trenti T (2022) Estimate false-negative RT-PCR rates for SARS-CoV-2. a systematic review and meta-analysis. Eur J Clin Investig 52(2):e13706","journal-title":"Eur J Clin Investig"},{"key":"5452_CR48","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"5452_CR49","doi-asserted-by":"crossref","first-page":"105532","DOI":"10.1016\/j.cmpb.2020.105532","volume":"194","author":"RM Pereira","year":"2020","unstructured":"Pereira RM, Bertolini D, Teixeira LO, Silla CN, Costa YM (2020) COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Comput Methods Progr Biomed 194:105532","journal-title":"Comput Methods Progr Biomed"},{"key":"5452_CR50","doi-asserted-by":"crossref","first-page":"100360","DOI":"10.1016\/j.imu.2020.100360","volume":"19","author":"M Rahimzadeh","year":"2020","unstructured":"Rahimzadeh M, Attar A (2020) A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50v2. Inf Med Unlocked 19:100360","journal-title":"Inf Med Unlocked"},{"key":"5452_CR51","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s12539-020-00403-6","volume":"13","author":"J Rasheed","year":"2021","unstructured":"Rasheed J, Hameed AA, Djeddi C, Jamil A, Al-Turjman F (2021) A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images. Interdiscip Sci Comput Life Sci 13:103\u2013117","journal-title":"Interdiscip Sci Comput Life Sci"},{"key":"5452_CR52","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/s42256-021-00307-0","volume":"3","author":"M Roberts","year":"2021","unstructured":"Roberts M, Driggs D, Thorpe M, Gilbey J, Yeung M, Ursprung S, Aviles-Rivero AI, Etmann C, McCague C, Beer L, Weir-McCall JR, Teng Z, Gkrania-Klotsas E, Rudd JHF, Sala E, Sch\u00f6nlieb C-B (2021) Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell 3:199\u2013217","journal-title":"Nat Mach Intell"},{"key":"5452_CR53","doi-asserted-by":"crossref","first-page":"W105","DOI":"10.1097\/RTI.0000000000000533","volume":"35","author":"S Schiaffino","year":"2020","unstructured":"Schiaffino S, Tritella S, Cozzi A, Carriero S, Blandi L, Ferraris L, Sardanelli F (2020) Diagnostic performance of chest X-ray for COVID-19 pneumonia during the SARS-CoV-2 pandemic in Lombardy, Italy. J Thorac Imag 35:W105\u2013W106","journal-title":"J Thorac Imag"},{"key":"5452_CR54","doi-asserted-by":"crossref","unstructured":"Sheikhi F, Alipour S (2021) A geometric algorithm for fault-tolerant classification of COVID-19 infected people. In 2021 26th International Computer Conference, Computer Society of Iran (CSICC), pp 1\u20135","DOI":"10.1109\/CSICC52343.2021.9420595"},{"key":"5452_CR55","doi-asserted-by":"crossref","unstructured":"Sheikhi F, Kowsari Z (2023) Time series forecasting of COVID-19 infections and deaths in alpha and delta variants using LSTM networks. To appear in PLOS ONE","DOI":"10.1371\/journal.pone.0282624"},{"issue":"5","key":"5452_CR56","doi-asserted-by":"crossref","first-page":"e0265489","DOI":"10.1371\/journal.pone.0265489","volume":"17","author":"F Sheikhi","year":"2022","unstructured":"Sheikhi F, Yousefian N, Tehranipoor P, Kowsari Z (2022) Estimation of the basic reproduction number of alpha and delta variants of COVID-19 pandemic in Iran. PLOS ONE 17(5):e0265489","journal-title":"PLOS ONE"},{"key":"5452_CR57","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.radi.2020.06.010","volume":"27","author":"BJ Stevens","year":"2021","unstructured":"Stevens BJ (2021) Reporting radiographers\u2019 interpretation and use of the British society of thoracic imaging\u2019s coding system when reporting COVID-19 chest x-rays. Radiography 27:90\u201394","journal-title":"Radiography"},{"issue":"12","key":"5452_CR58","doi-asserted-by":"crossref","first-page":"2106","DOI":"10.3844\/jcssp.2012.2106.2111","volume":"8","author":"A Suresh","year":"2013","unstructured":"Suresh A, Shunmuganathan KL (2013) Feature fusion technique for colour texture classification system based on gray level co-occurrence matrix. J Comput Sci 8(12):2106\u20132111","journal-title":"J Comput Sci"},{"key":"5452_CR59","unstructured":"Thompson NC, Greenewald KH, Lee K, Manso GF (2020) The computational limits of deep learning. CoRR, abs\/2007.05558"},{"key":"5452_CR60","unstructured":"Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, J\u00e9gou H (2021) Training data-efficient image transformers & distillation through attention. arXiv:2012.12877v2"},{"key":"5452_CR61","doi-asserted-by":"crossref","unstructured":"Ukwuoma CC,Qin Z, Belal Bin Heyat M, Akhtar F, Bamisile O, Muaad AY, Addo D, Al-antari MA (2022) A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images. J Adv Res 48:191\u2013211","DOI":"10.1016\/j.jare.2022.08.021"},{"key":"5452_CR62","doi-asserted-by":"crossref","unstructured":"Ukwuoma CC, Qin Z, Heyat MBB, Akhtar F, Smahi A, Jackson JK, Furqan\u00a0Qadri S, Muaad AY, Monday HN, Nneji GU (2022) Automated lung-related pneumonia and COVID-19 detection based on novel feature extraction framework and vision transformer approaches using chest X-ray images. Bioengineering, 9(11)709","DOI":"10.3390\/bioengineering9110709"},{"key":"5452_CR63","doi-asserted-by":"crossref","first-page":"e453","DOI":"10.7717\/peerj.453","volume":"2","author":"S van der Walt","year":"2014","unstructured":"van der Walt S, Sch\u00f6nberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, Gouillart E, Scikit-image TYu (2014) Image processing in python. PeerJ 2:e453","journal-title":"PeerJ"},{"key":"5452_CR64","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.ins.2020.09.041","volume":"545","author":"S Varela-Santos","year":"2021","unstructured":"Varela-Santos S, Melin P (2021) A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks. Inf Sci 545:403\u2013414","journal-title":"Inf Sci"},{"issue":"7","key":"5452_CR65","doi-asserted-by":"crossref","first-page":"660","DOI":"10.3390\/life11070660","volume":"11","author":"R Verna","year":"2021","unstructured":"Verna R, Alallon W, Murakami M, Hayward CPM, Harrath AH, Alwasel SH, Sumita NM, Alatas O, Fedeli V, Sharma P, Fuso A, Capuano DM, Capalbo M, Angeloni A, Bizzarri M (2021) Analytical performance of COVID-19 detection methods (RT-PCR): scientific and societal concerns. Life 11(7):660","journal-title":"Life"},{"issue":"19549","key":"5452_CR66","first-page":"1","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang L, Lin ZQ, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep 10(19549):1\u20132","journal-title":"Sci Rep"},{"issue":"1","key":"5452_CR67","first-page":"19638","volume":"11","author":"D Yang","year":"2021","unstructured":"Yang D, Martinez C, Visu\u00f1a L, Khandhar H, Bhatt C, Carretero J (2021) Detection and analysis of COVID-19 in medical images using deep learning techniques. Sci Rep Nat 11(1):19638","journal-title":"Sci Rep Nat"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05452-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05452-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05452-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T13:12:53Z","timestamp":1702645973000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05452-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,21]]},"references-count":67,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["5452"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05452-4","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,21]]},"assertion":[{"value":"29 May 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}