{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T17:53:40Z","timestamp":1778867620575,"version":"3.51.4"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"34","license":[{"start":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T00:00:00Z","timestamp":1709856000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T00:00:00Z","timestamp":1709856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100020607","name":"Hitit University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100020607","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The novel coronavirus disease has caused severe threats to the daily life and health of people all over the world. Hence, early detection and timely treatment of this disease are significant to prevent the coronavirus's spread and ensure more effective patient care. This work adopted an integrated framework comprising deep learning and attention mechanism to provide a more effective and reliable diagnosis. This framework consists of two convolution neural network (CNN), a bidirectional LSTM, two fully-connected layers (FCL), and an attention mechanism. The main aim of the proposed framework is to reveal a promising approach based on deep learning for early and timely detection of coronavirus disease. For greater accuracy, the framework's hyperparameters are tuned by means of a genetic algorithm. The effectiveness of the proposed framework has been examined utilizing a public dataset including 18 different blood findings from Albert Einstein Israelita Hospital in Sao Paulo, Brazil. Additionally, within the experimental studies, the proposed framework is subjected to comparison with the state-of-the-art techniques, evaluated across various metrics. Based on the derived consequences, the proposed framework has yielded enhancements in accuracy, recall, precision, and F1-score, registering approximate improvements of 1.27%, 4.07%, 3.20%, and 2.88%, respectively, as measured against the second-best rates.<\/jats:p>","DOI":"10.1007\/s11042-024-18850-4","type":"journal-article","created":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T06:29:26Z","timestamp":1709879366000},"page":"81477-81490","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Accurate detection of coronavirus cases using deep learning with attention mechanism and genetic algorithm"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1590-0023","authenticated-orcid":false,"given":"Ahmet","family":"Kara","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,8]]},"reference":[{"key":"18850_CR1","doi-asserted-by":"publisher","first-page":"115616","DOI":"10.1016\/j.eswa.2021.115616","volume":"185","author":"Z Li","year":"2021","unstructured":"Li Z, Zhao S, Chen Y et al (2021) A deep-learning-based framework for severity assessment of COVID-19 with CT images. Expert Syst Appl 185:115616. https:\/\/doi.org\/10.1016\/j.eswa.2021.115616","journal-title":"Expert Syst Appl"},{"key":"18850_CR2","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1002\/SPE.3011","volume":"52","author":"A Singh","year":"2022","unstructured":"Singh A, Kaur A, Dhillon A et al (2022) Software system to predict the infection in COVID-19 patients using deep learning and web of things. Softw Pract Exp 52:868\u2013886. https:\/\/doi.org\/10.1002\/SPE.3011","journal-title":"Softw Pract Exp"},{"key":"18850_CR3","doi-asserted-by":"publisher","unstructured":"Afif M, Ayachi R, Said Y, Atri M (2023) Deep learning-based technique for lesions segmentation in CT scan images for COVID-19 prediction. Multimed Tools Appl 1\u201315. https:\/\/doi.org\/10.1007\/S11042-023-14941-W\/FIGURES\/7","DOI":"10.1007\/S11042-023-14941-W\/FIGURES\/7"},{"key":"18850_CR4","doi-asserted-by":"publisher","first-page":"13525","DOI":"10.1007\/s00521-021-05976-x","volume":"33","author":"A Kara","year":"2021","unstructured":"Kara A (2021) A data-driven approach based on deep neural networks for lithium-ion battery prognostics. Neural Comput Appl 33:13525\u201313538. https:\/\/doi.org\/10.1007\/s00521-021-05976-x","journal-title":"Neural Comput Appl"},{"key":"18850_CR5","doi-asserted-by":"publisher","first-page":"37461","DOI":"10.1007\/S11042-022-13504-9\/TABLES\/4","volume":"81","author":"K Yadav","year":"2022","unstructured":"Yadav K, Tiwari S, Jain A, Alshudukhi J (2022) Convolution neural network based model to classify colon cancerous tissue. Multimed Tools Appl 81:37461\u201337476. https:\/\/doi.org\/10.1007\/S11042-022-13504-9\/TABLES\/4","journal-title":"Multimed Tools Appl"},{"key":"18850_CR6","doi-asserted-by":"publisher","DOI":"10.1002\/SPE.3247","author":"A Pati","year":"2023","unstructured":"Pati A, Parhi M, Pattanayak BK, Pati B (2023) IFCnCov: An IoT-based smart diagnostic architecture for COVID-19. Softw Pract Exp. https:\/\/doi.org\/10.1002\/SPE.3247","journal-title":"Softw Pract Exp"},{"key":"18850_CR7","doi-asserted-by":"publisher","first-page":"14243","DOI":"10.1007\/S00521-023-08484-2\/TABLES\/4","volume":"35","author":"M Rana","year":"2023","unstructured":"Rana M, Bhushan M (2023) Classifying breast cancer using transfer learning models based on histopathological images. Neural Comput Appl 35:14243\u201314257. https:\/\/doi.org\/10.1007\/S00521-023-08484-2\/TABLES\/4","journal-title":"Neural Comput Appl"},{"key":"18850_CR8","doi-asserted-by":"publisher","first-page":"e7662","DOI":"10.1002\/CPE.7662","volume":"35","author":"B Cao","year":"2023","unstructured":"Cao B, Liu J (2023) Combining bidirectional long short-term memory and self-attention mechanism for code search. Concurr Comput 35:e7662. https:\/\/doi.org\/10.1002\/CPE.7662","journal-title":"Concurr Comput"},{"key":"18850_CR9","doi-asserted-by":"publisher","first-page":"105233","DOI":"10.1016\/j.compbiomed.2022.105233","volume":"143","author":"N Subramanian","year":"2022","unstructured":"Subramanian N, Elharrouss O, Al-Maadeed S, Chowdhury M (2022) A review of deep learning-based detection methods for COVID-19. Comput Biol Med 143:105233. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105233","journal-title":"Comput Biol Med"},{"key":"18850_CR10","doi-asserted-by":"publisher","first-page":"30551","DOI":"10.1109\/ACCESS.2021.3058537","volume":"9","author":"MM Islam","year":"2021","unstructured":"Islam MM, Karray F, Alhajj R, Zeng J (2021) A review on deep learning techniques for the diagnosis of novel coronavirus (COVID-19). IEEE Access 9:30551\u201330572. https:\/\/doi.org\/10.1109\/ACCESS.2021.3058537","journal-title":"IEEE Access"},{"key":"18850_CR11","doi-asserted-by":"publisher","first-page":"3414","DOI":"10.3390\/app11083414","volume":"11","author":"A Rehman","year":"2021","unstructured":"Rehman A, Iqbal MA, Xing H, Ahmed I (2021) COVID-19 Detection empowered with machine learning and deep learning techniques: A systematic review. Appl Sci 11:3414. https:\/\/doi.org\/10.3390\/app11083414","journal-title":"Appl Sci"},{"key":"18850_CR12","doi-asserted-by":"publisher","first-page":"100449","DOI":"10.1016\/j.imu.2020.100449","volume":"21","author":"M AlJame","year":"2020","unstructured":"AlJame M, Ahmad I, Imtiaz A, Mohammed A (2020) Ensemble learning model for diagnosing COVID-19 from routine blood tests. Inform Med Unlocked 21:100449. https:\/\/doi.org\/10.1016\/j.imu.2020.100449","journal-title":"Inform Med Unlocked"},{"key":"18850_CR13","doi-asserted-by":"publisher","first-page":"10738","DOI":"10.1038\/s41598-021-90265-9","volume":"11","author":"M Kukar","year":"2021","unstructured":"Kukar M, Gun\u010dar G, Vovko T et al (2021) COVID-19 diagnosis by routine blood tests using machine learning. Sci Rep 11:10738. https:\/\/doi.org\/10.1038\/s41598-021-90265-9","journal-title":"Sci Rep"},{"key":"18850_CR14","doi-asserted-by":"publisher","first-page":"100412","DOI":"10.1016\/j.imu.2020.100412","volume":"20","author":"MZ Islam","year":"2020","unstructured":"Islam MZ, Islam MM, Asraf A (2020) A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Inform Med Unlocked 20:100412. https:\/\/doi.org\/10.1016\/j.imu.2020.100412","journal-title":"Inform Med Unlocked"},{"key":"18850_CR15","doi-asserted-by":"publisher","first-page":"106912","DOI":"10.1016\/j.asoc.2020.106912","volume":"98","author":"MF Aslan","year":"2021","unstructured":"Aslan MF, Unlersen MF, Sabanci K, Durdu A (2021) CNN-based transfer learning\u2013BiLSTM network: A novel approach for COVID-19 infection detection. Appl Soft Comput 98:106912. https:\/\/doi.org\/10.1016\/j.asoc.2020.106912","journal-title":"Appl Soft Comput"},{"key":"18850_CR16","doi-asserted-by":"publisher","first-page":"3104","DOI":"10.1007\/s10489-021-02199-4","volume":"51","author":"P Sab","year":"2021","unstructured":"Sab P, Annavarapu CSR (2021) Deep learning-based improved snapshot ensemble technique for COVID-19 chest X-ray classification. Appl Intell 51:3104\u20133120. https:\/\/doi.org\/10.1007\/s10489-021-02199-4","journal-title":"Appl Intell"},{"key":"18850_CR17","doi-asserted-by":"publisher","first-page":"3798","DOI":"10.3390\/ELECTRONICS1122","volume":"11","author":"M Zivkovic","year":"2022","unstructured":"Zivkovic M, Bacanin N, Antonijevic M et al (2022) Hybrid CNN and XGBoost model tuned by modified arithmetic optimization algorithm for COVID-19 early diagnostics from x-ray images. Electronics 2022 11:3798. https:\/\/doi.org\/10.3390\/ELECTRONICS1122","journal-title":"Electronics 2022"},{"key":"18850_CR18","unstructured":"Kara A (2021) A Deep learning-based approach for effective diagnosis of coronavirus disease using clinical data. In: International Conference on Engineering Technologies (ICENTE 2021). 106\u2013109"},{"key":"18850_CR19","doi-asserted-by":"publisher","first-page":"107878","DOI":"10.1016\/j.asoc.2021.107878","volume":"113","author":"K Shankar","year":"2021","unstructured":"Shankar K, Perumal E, D\u00edaz VG et al (2021) An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images. Appl Soft Comput 113:107878. https:\/\/doi.org\/10.1016\/j.asoc.2021.107878","journal-title":"Appl Soft Comput"},{"key":"18850_CR20","doi-asserted-by":"publisher","first-page":"104348","DOI":"10.1016\/j.compbiomed.2021.104348","volume":"132","author":"DM Ibrahim","year":"2021","unstructured":"Ibrahim DM, Elshennawy NM, Sarhan AM (2021) Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Comput Biol Med 132:104348. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104348","journal-title":"Comput Biol Med"},{"key":"18850_CR21","doi-asserted-by":"publisher","first-page":"107329","DOI":"10.1016\/j.asoc.2021.107329","volume":"106","author":"V G\u00f6reke","year":"2021","unstructured":"G\u00f6reke V, Sar\u0131 V, Kockanat S (2021) A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings. Appl Soft Comput 106:107329. https:\/\/doi.org\/10.1016\/j.asoc.2021.107329","journal-title":"Appl Soft Comput"},{"key":"18850_CR22","doi-asserted-by":"publisher","first-page":"110120","DOI":"10.1016\/j.chaos.2020.110120","volume":"140","author":"TB Alakus","year":"2020","unstructured":"Alakus TB, Turkoglu I (2020) Comparison of deep learning approaches to predict COVID-19 infection. Chaos Solitons Fractals 140:110120. https:\/\/doi.org\/10.1016\/j.chaos.2020.110120","journal-title":"Chaos Solitons Fractals"},{"key":"18850_CR23","doi-asserted-by":"publisher","first-page":"e6988","DOI":"10.1002\/CPE.6988","volume":"34","author":"A Kara","year":"2022","unstructured":"Kara A (2022) A deep learning framework with convolutional long short-term memory for influenza-like illness trend estimation. Concurr Comput 34:e6988. https:\/\/doi.org\/10.1002\/CPE.6988","journal-title":"Concurr Comput"},{"key":"18850_CR24","unstructured":"Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings"},{"key":"18850_CR25","doi-asserted-by":"publisher","first-page":"10930","DOI":"10.1038\/s41598-021-90428-8","volume":"11","author":"R Ranjbarzadeh","year":"2021","unstructured":"Ranjbarzadeh R, Bagherian Kasgari A, Jafarzadeh Ghoushchi S et al (2021) Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci Rep 11:10930. https:\/\/doi.org\/10.1038\/s41598-021-90428-8","journal-title":"Sci Rep"},{"key":"18850_CR26","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1002\/ima.22527","volume":"31","author":"T Zhou","year":"2021","unstructured":"Zhou T, Canu S, Ruan S (2021) Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism. Int J Imaging Syst Technol 31:16\u201327. https:\/\/doi.org\/10.1002\/ima.22527","journal-title":"Int J Imaging Syst Technol"},{"key":"18850_CR27","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neucom.2021.03.034","volume":"443","author":"Y Xu","year":"2021","unstructured":"Xu Y, Lam H-K, Jia G (2021) MANet: A two-stage deep learning method for classification of COVID-19 from Chest X-ray images. Neurocomputing 443:96\u2013105. https:\/\/doi.org\/10.1016\/j.neucom.2021.03.034","journal-title":"Neurocomputing"},{"key":"18850_CR28","doi-asserted-by":"publisher","first-page":"104834","DOI":"10.1016\/j.compbiomed.2021.104834","volume":"137","author":"T Wu","year":"2021","unstructured":"Wu T, Tang C, Xu M et al (2021) ULNet for the detection of coronavirus (COVID-19) from chest X-ray images. Comput Biol Med 137:104834. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104834","journal-title":"Comput Biol Med"},{"key":"18850_CR29","doi-asserted-by":"publisher","first-page":"103863","DOI":"10.1016\/J.DSP.2022.103863","volume":"133","author":"J Song","year":"2023","unstructured":"Song J, Zhang R (2023) A novel computer-assisted diagnosis method of knee osteoarthritis based on multivariate information and deep learning model. Digit Signal Process 133:103863. https:\/\/doi.org\/10.1016\/J.DSP.2022.103863","journal-title":"Digit Signal Process"},{"key":"18850_CR30","doi-asserted-by":"publisher","unstructured":"Zhang X, Ma Y, Wang M (2023) An attention-based Logistic-CNN-BiLSTM hybrid neural network for credit risk prediction of listed real estate enterprises. Expert Syst e13299. https:\/\/doi.org\/10.1111\/EXSY.13299","DOI":"10.1111\/EXSY.13299"},{"key":"18850_CR31","doi-asserted-by":"publisher","first-page":"216","DOI":"10.35377\/SAUCIS.04.02.912154","volume":"4","author":"A Kara","year":"2021","unstructured":"Kara A (2021) A hybrid prognostic approach based on deep learning for the degradation prediction of machinery. Sakarya Univ J Comput Inform Sci 4:216\u2013226. https:\/\/doi.org\/10.35377\/SAUCIS.04.02.912154","journal-title":"Sakarya Univ J Comput Inform Sci"},{"key":"18850_CR32","doi-asserted-by":"publisher","unstructured":"Yousaf K, Nawaz T (2023) An attention mechanism-based CNN-BiLSTM classification model for detection of inappropriate content in cartoon videos. Multimed Tools Appl 1\u201324. https:\/\/doi.org\/10.1007\/S11042-023-16727-6\/TABLES\/8","DOI":"10.1007\/S11042-023-16727-6\/TABLES\/8"},{"key":"18850_CR33","doi-asserted-by":"publisher","first-page":"2419","DOI":"10.1007\/S11042-022-13329-6\/FIGURES\/8","volume":"82","author":"M Majidi","year":"2023","unstructured":"Majidi M, Toroghi RM (2023) A combination of multi-objective genetic algorithm and deep learning for music harmony generation. Multimed Tools Appl 82:2419\u20132435. https:\/\/doi.org\/10.1007\/S11042-022-13329-6\/FIGURES\/8","journal-title":"Multimed Tools Appl"},{"key":"18850_CR34","first-page":"2171","volume":"13","author":"FA Fortin","year":"2012","unstructured":"Fortin FA, De Rainville FM, Gardner MA et al (2012) DEAP: Evolutionary algorithms made easy. J Mach Learn Res 13:2171\u20132175","journal-title":"J Mach Learn Res"},{"key":"18850_CR35","doi-asserted-by":"publisher","first-page":"103263","DOI":"10.1016\/J.BSPC.2021.103263","volume":"72","author":"S Babaei Rikan","year":"2022","unstructured":"Babaei Rikan S, Sorayaie Azar A, Ghafari A et al (2022) COVID-19 diagnosis from routine blood tests using artificial intelligence techniques. Biomed Signal Process Control 72:103263. https:\/\/doi.org\/10.1016\/J.BSPC.2021.103263","journal-title":"Biomed Signal Process Control"},{"key":"18850_CR36","doi-asserted-by":"publisher","first-page":"100941","DOI":"10.1016\/J.IMU.2022.100941","volume":"30","author":"M Rostami","year":"2022","unstructured":"Rostami M, Oussalah M (2022) A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest. Inform Med Unlocked 30:100941. https:\/\/doi.org\/10.1016\/J.IMU.2022.100941","journal-title":"Inform Med Unlocked"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18850-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18850-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18850-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T12:10:53Z","timestamp":1728475853000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18850-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,8]]},"references-count":36,"journal-issue":{"issue":"34","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["18850"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18850-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,8]]},"assertion":[{"value":"3 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2024","order":4,"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 was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare that there is no conflict of interest regarding the publication of this article.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}