{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T09:56:18Z","timestamp":1765878978837,"version":"3.48.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,14]],"date-time":"2025-12-14T00:00:00Z","timestamp":1765670400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-01054-5","type":"journal-article","created":{"date-parts":[[2025,12,14]],"date-time":"2025-12-14T07:06:04Z","timestamp":1765695964000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pulmonary Function Testing Based Severity Level Identification Using ANLipBNFIS and Lung Disease Classification Using B2SRS-VGG16"],"prefix":"10.1007","volume":"18","author":[{"given":"N.","family":"Gobalakrishnan","sequence":"first","affiliation":[]},{"given":"N.","family":"Gobinathan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,14]]},"reference":[{"issue":"7\u20138","key":"1054_CR1","doi-asserted-by":"publisher","first-page":"6219","DOI":"10.1007\/s00500-023-09480-3","volume":"28","author":"S Ashwini","year":"2023","unstructured":"Ashwini, S., Arunkumar, J.R., Prabu, R.T., Singh, N.H., Singh, N.P.: Diagnosis and multi-classification of lung diseases in CXR images using optimized deep convolutional neural network. Soft. Comput. 28(7\u20138), 6219\u20136233 (2023). https:\/\/doi.org\/10.1007\/s00500-023-09480-3","journal-title":"Soft. Comput."},{"issue":"1","key":"1054_CR2","doi-asserted-by":"publisher","first-page":"48","DOI":"10.3390\/j7010003","volume":"7","author":"MV Sanida","year":"2024","unstructured":"Sanida, M.V., Sanida, T., Sideris, A., Dasygenis, M.: An advanced deep learning framework for multi-class diagnosis from chest X-ray images. Multidiscip. Sci. J. 7(1), 48\u201371 (2024). https:\/\/doi.org\/10.3390\/j7010003","journal-title":"Multidiscip. Sci. J."},{"issue":"1","key":"1054_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s44163-024-00110-x","volume":"4","author":"G Mohan","year":"2024","unstructured":"Mohan, G., Subashini, M.M., Balan, S., Singh, S.: A multiclass deep learning algorithm for healthy lung, COVID-19 and pneumonia disease detection from chest X-ray images. Discov. Artif. Intell. 4(1), 1\u201315 (2024). https:\/\/doi.org\/10.1007\/s44163-024-00110-x","journal-title":"Discov. Artif. Intell."},{"issue":"21","key":"1054_CR4","doi-asserted-by":"publisher","first-page":"60789","DOI":"10.1007\/s11042-023-17965-4","volume":"83","author":"NW Asnake","year":"2024","unstructured":"Asnake, N.W., Salau, A.O., Ayalew, A.M.: X-ray image-based pneumonia detection and classification using deep learning. Multimed. Tools Appl. 83(21), 60789\u201360807 (2024). https:\/\/doi.org\/10.1007\/s11042-023-17965-4","journal-title":"Multimed. Tools Appl."},{"issue":"19","key":"1054_CR5","doi-asserted-by":"publisher","first-page":"11535","DOI":"10.1007\/s00500-024-09901-x","volume":"28","author":"S Agarwal","year":"2024","unstructured":"Agarwal, S., Arya, K.V., Meena, Y.K.: Multifusionnet: multilayer multimodal fusion of deep neural networks for chest X-ray image classification. Soft. Comput. 28(19), 11535\u201311551 (2024)","journal-title":"Soft. Comput."},{"issue":"1","key":"1054_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.2401.00728","volume":"24","author":"D Li","year":"2024","unstructured":"Li, D.: Attention-enhanced architecture for improved pneumonia detection in chest X-ray images. BMC Med. Imag. 24(1), 1\u201313 (2024). https:\/\/doi.org\/10.48550\/arXiv.2401.00728","journal-title":"BMC Med. Imag."},{"issue":"9","key":"1054_CR7","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.1016\/j.acra.2022.11.027","volume":"30","author":"ML Huang","year":"2023","unstructured":"Huang, M.L., Liao, Y.C.: Stacking ensemble and ECA-EfficientNetV2 convolutional neural networks on classification of multiple chest diseases including COVID-19. Acad. Radiol. 30(9), 1915\u20131935 (2023). https:\/\/doi.org\/10.1016\/j.acra.2022.11.027","journal-title":"Acad. Radiol."},{"issue":"4","key":"1054_CR8","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1080\/24751839.2024.2317509","volume":"8","author":"TA Pham","year":"2024","unstructured":"Pham, T.A., Hoang, V.D.: Chest x-ray image classification using transfer learning and hyperparameter customization for lung disease diagnosis. J. Inf. Telecommun. 8(4), 587\u2013601 (2024). https:\/\/doi.org\/10.1080\/24751839.2024.2317509","journal-title":"J. Inf. Telecommun."},{"key":"1054_CR9","doi-asserted-by":"publisher","first-page":"116202","DOI":"10.1109\/ACCESS.2024.3440577","volume":"12","author":"A Tripathi","year":"2024","unstructured":"Tripathi, A., Singh, T., Nair, R.R., Duraisamy, P.: Improving early detection and classification of lung diseases with innovative MobileNetV2 framework. IEEE Access 12, 116202\u2013116217 (2024)","journal-title":"IEEE Access"},{"issue":"4","key":"1054_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ACCESS.2024.3440577","volume":"14","author":"Q An","year":"2024","unstructured":"An, Q., Chen, W., Shao, W.: A deep convolutional neural network for pneumonia detection in X-ray images with attention ensemble. Diagnostics 14(4), 1\u201323 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3440577","journal-title":"Diagnostics"},{"key":"1054_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.imu.2024.101448","volume":"45","author":"AM Ayalew","year":"2024","unstructured":"Ayalew, A.M., Bezabih, Y.A., Abuhayi, B.M., Ayalew, A.Y.: Atelectasis detection in chest X-ray images using convolutional neural networks and transfer learning with anisotropic diffusion filter. Inform. Med. Unlocked 45, 1\u20139 (2024). https:\/\/doi.org\/10.1016\/j.imu.2024.101448","journal-title":"Inform. Med. Unlocked"},{"key":"1054_CR12","doi-asserted-by":"publisher","unstructured":"\u00d6zt\u00fcrk, \u015e., Tural\u0131, M.Y., \u00c7ukur, T.: HydraViT: adaptive multi-branch transformer for multi-label disease classification from chest X-ray images. arXiv, 1\u201310 (2023). https:\/\/doi.org\/10.1016\/j.bspc.2024.106959","DOI":"10.1016\/j.bspc.2024.106959"},{"key":"1054_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dajour.2024.100458","volume":"11","author":"RK Bondugula","year":"2024","unstructured":"Bondugula, R.K., Bommi, N.S., Udgata, S.K.: An efficient multi-stage ensemble deep learning framework for diagnosing infectious diseases. Decis. Anal. J. 11, 1\u201310 (2024). https:\/\/doi.org\/10.1016\/j.dajour.2024.100458","journal-title":"Decis. Anal. J."},{"key":"1054_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/diagnostics15131728","volume":"15","author":"H Aljuaid","year":"2025","unstructured":"Aljuaid, H., Albalahad, H., Alshuaibi, W., Almutairi, S., Aljohani, T.H., Hussain, N., Mohammad, F.: RADAI: a deep learning-based classification of lung abnormalities in chest X-rays. Diagnostics 15, 1\u201317 (2025). https:\/\/doi.org\/10.3390\/diagnostics15131728","journal-title":"Diagnostics"},{"key":"1054_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.iswa.2023.200315","volume":"21","author":"AG Argho","year":"2024","unstructured":"Argho, A.G., Maswood, M.M.S., Mahmood, M.I., Mondol, N.: EfficientCovNet: a CNN-based approach to detect various pulmonary diseases including COVID-19 using modified EfficientNet. Intell. Syst. Appl. 21, 1\u201312 (2024). https:\/\/doi.org\/10.1016\/j.iswa.2023.200315","journal-title":"Intell. Syst. Appl."},{"issue":"1","key":"1054_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-024-02591-3","volume":"24","author":"J Ko","year":"2024","unstructured":"Ko, J., Park, S., Woo, H.G.: Optimization of vision transformer-based detection of lung diseases from chest X-ray images. BMC Med. Inform. Decis. Mak. 24(1), 1\u20138 (2024). https:\/\/doi.org\/10.1186\/s12911-024-02591-3","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"1054_CR17","doi-asserted-by":"publisher","unstructured":"Kabiraj, A., Meena, T., Reddy, P.B., Roy, S.: Detection and classification of lung disease using deep learning architecture from X-ray images. In: International Symposium on Visual Computing. Springer, Cham. (2022). https:\/\/doi.org\/10.1007\/978-3-031-20713-6_34","DOI":"10.1007\/978-3-031-20713-6_34"},{"issue":"8","key":"1054_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.35940\/ijitee.G9037.0610821","volume":"10","author":"S Tripathi","year":"2021","unstructured":"Tripathi, S., Shetty, S., Jain, S., Sharma, V.: Lung disease detection using deep learning. Int. J. Innov. Technol. Explor. Eng. 10(8), 1\u20138 (2021)","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"1054_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compbiomed.2023.106646","volume":"155","author":"FJM Shamrat","year":"2023","unstructured":"Shamrat, F.J.M., Azam, S., Karim, A., Ahmed, K., Bui, F.M., De Boer, F.: High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images. Comput. Biol. Med. 155, 1\u201314 (2023). https:\/\/doi.org\/10.1016\/j.compbiomed.2023.106646","journal-title":"Comput. Biol. Med."},{"key":"1054_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2023.120528","volume":"229","author":"M Nahiduzzaman","year":"2023","unstructured":"Nahiduzzaman, M., Goni, M.O.F., Hassan, R., Islam, M.R., Syfullah, M.K., Shahriar, S.M., Anower, M.S., Ahsan, M., Haider, J., Kowalski, M.: Parallel CNN-ELM: a multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19. Expert Syst. Appl. 229, 1\u201319 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.120528","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"1054_CR21","doi-asserted-by":"publisher","first-page":"3121","DOI":"10.1007\/s40747-021-00474-y","volume":"8","author":"KV Priya","year":"2021","unstructured":"Priya, K.V., Peter, J.D.: A federated approach for detecting the chest diseases using DenseNet for multi-label classification. Complex Intell. Syst. 8(4), 3121\u20133129 (2021). https:\/\/doi.org\/10.1007\/s40747-021-00474-y","journal-title":"Complex Intell. Syst."},{"issue":"4","key":"1054_CR22","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1007\/s12559-020-09787-5","volume":"16","author":"AU Ibrahim","year":"2024","unstructured":"Ibrahim, A.U., Ozsoz, M., Serte, S., Al-Turjman, F., Yakoi, P.S.: Pneumonia classification using deep learning from chest X-ray images during COVID-19. Cogn. Comput. 16(4), 1589\u20131601 (2024). https:\/\/doi.org\/10.1007\/s12559-020-09787-5","journal-title":"Cogn. Comput."},{"key":"1054_CR23","doi-asserted-by":"publisher","first-page":"118576","DOI":"10.1016\/j.eswa.2022.118576","volume":"211","author":"M Nahiduzzaman","year":"2023","unstructured":"Nahiduzzaman, M., Islam, M.R., Hassan, R.: ChestX-Ray6: prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network. Exp. Syst. Appl. 211, 118576 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2022.118576","journal-title":"Exp. Syst. Appl."},{"key":"1054_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artmed.2025.103135","volume":"165","author":"A Hage Chehade","year":"2025","unstructured":"Hage Chehade, A., Abdallah, N., Marion, J.M., Hatt, M., Oueidat, M., Chauvet, P.: Reconstruction-based approach for chest X-ray image segmentation and enhanced multi-label chest disease classification. Artif. Intell. Med. 165, 1\u201316 (2025). https:\/\/doi.org\/10.1016\/j.artmed.2025.103135","journal-title":"Artif. Intell. Med."},{"issue":"1","key":"1054_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-024-01018-0","volume":"11","author":"T Geroski","year":"2024","unstructured":"Geroski, T., Pavi\u0107, O., Da\u0161i\u0107, L., Milovanovi\u0107, D., Petrovi\u0107, M., Filipovi\u0107, N.: SoftLungX: leveraging transfer learning with convolutional neural networks for accurate respiratory disease classification in chest X-ray images. J. Big Data 11(1), 1\u201319 (2024). https:\/\/doi.org\/10.1186\/s40537-024-01018-0","journal-title":"J. Big Data"},{"key":"1054_CR26","doi-asserted-by":"publisher","unstructured":"Markose, G.C., Sitaraman, S.R., Kumar, S.V., Patel, V., Mohammed, R.J., Vaghela, C. Utilizing machine learning for lung disease diagnosis. In: 2024 3rd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON), pp. 1\u20136. IEEE. (2024). https:\/\/doi.org\/10.1109\/ODICON62106.2024.10797552.","DOI":"10.1109\/ODICON62106.2024.10797552"},{"key":"1054_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106783","volume":"158","author":"A Kumar","year":"2023","unstructured":"Kumar, A., Singh, R., Verma, P., Sharma, S.: Hybrid CNN\u2013ResNet approach for multi-class chest X-ray disease classification. Comput. Biol. Med. 158, 106783 (2023). https:\/\/doi.org\/10.1016\/j.compbiomed.2023.106783","journal-title":"Comput. Biol. Med."},{"key":"1054_CR28","doi-asserted-by":"publisher","first-page":"87234","DOI":"10.1109\/ACCESS.2022.3192345","volume":"10","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., Li, H., Wang, J., Chen, X.: Attention-based CNN framework for pulmonary disease detection from chest X-rays. IEEE Access 10, 87234\u201387245 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3192345","journal-title":"IEEE Access"},{"key":"1054_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122980","volume":"243","author":"L Chen","year":"2024","unstructured":"Chen, L., Zhao, Q., Liu, Y.: Fuzzy logic-driven hybrid neural networks for medical image interpretation. Expert Syst. Appl. 243, 122980 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2023.122980","journal-title":"Expert Syst. Appl."},{"key":"1054_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104850","volume":"85","author":"M Rahman","year":"2023","unstructured":"Rahman, M., Hasan, M., Alam, M.: Deep learning-based severity assessment in chest radiography using adaptive neuro-fuzzy inference systems. Biomed. Signal Process. Control 85, 104850 (2023). https:\/\/doi.org\/10.1016\/j.bspc.2023.104850","journal-title":"Biomed. Signal Process. Control"},{"key":"1054_CR31","doi-asserted-by":"publisher","unstructured":"Zhou, T. Ye, X., Lu, H., Zheng, X., Qiu, S., Liu, Y.: Dense convolutional network and its application in medical image analysis. In: Proc. 2024 Int. Conf. Emerging Techniques in Computational Intelligence (ICETCI), pp. 426\u2013431. (2024). https:\/\/doi.org\/10.1109\/ICETCI62771.2024.10704103.","DOI":"10.1109\/ICETCI62771.2024.10704103"},{"issue":"2","key":"1054_CR32","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s12530-025-09686-w","volume":"16","author":"E Hassan","year":"2025","unstructured":"Hassan, E., Saber, A., El-Sappagh, S., El-Rashidy, N.: Optimized ensemble deep learning approach for accurate breast cancer diagnosis using transfer learning and grey wolf optimization. Evol. Syst. 16(2), 59 (2025). https:\/\/doi.org\/10.1007\/s12530-025-09686-w","journal-title":"Evol. Syst."},{"key":"1054_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.108128","volume":"110","author":"E Hassan","year":"2025","unstructured":"Hassan, E., Saber, A., Abd El-Hafeez, T., Medhat, T., Shams, M.Y.: Enhanced dysarthria detection in cerebral palsy and ALS patients using WaveNet and CNN-BiLSTM models: a comparative study with model interpretability. Biomed. Signal Process. Control 110, 108128 (2025). https:\/\/doi.org\/10.1016\/j.bspc.2025.108128","journal-title":"Biomed. Signal Process. Control"},{"key":"1054_CR34","unstructured":"Dataset: Lung Disease Dataset. https:\/\/www.kaggle.com\/datasets\/klu2000030172\/lung-disease-dataset\/data"},{"issue":"9","key":"1054_CR35","doi-asserted-by":"publisher","first-page":"2441","DOI":"10.3390\/biomedicines11092441","volume":"11","author":"NP Tas","year":"2023","unstructured":"Tas, N.P., Kaya, O., Macin, G., Tasci, B., Dogan, S., Tuncer, T.: ASNET: a novel AI framework for accurate ankylosing spondylitis diagnosis from MRI. Biomedicines 11(9), 2441 (2023). https:\/\/doi.org\/10.3390\/biomedicines11092441","journal-title":"Biomedicines"},{"key":"1054_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2023.103483","volume":"123","author":"B Ta\u015fc\u0131","year":"2023","unstructured":"Ta\u015fc\u0131, B., Acharya, M.R., Baygin, M., Dogan, S., Tuncer, T., Belhaouari, S.B.: InCR: inception and concatenation residual block-based deep learning network for damaged building detection using remote sensing images. Int. J. Appl. Earth Obs. Geoinf. 123, 103483 (2023). https:\/\/doi.org\/10.1016\/j.jag.2023.103483","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"1054_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112235","volume":"301","author":"Y Wu","year":"2024","unstructured":"Wu, Y., Hu, Y., Yin, S., Cai, B., Tang, X.: A graph convolutional network model based on regular equivalence for identifying influential nodes in complex networks. Knowl.-Based Syst. 301, 112235 (2024). https:\/\/doi.org\/10.1016\/j.knosys.2024.112235","journal-title":"Knowl.-Based Syst."},{"key":"1054_CR38","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-84504-y","volume":"15","author":"S Anari","year":"2025","unstructured":"Anari, S., Sadeghi, S., Sheikhi, G., Ranjbarzadeh, R., Bendechache, M.: Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models. Sci. Rep. 15, 1027 (2025). https:\/\/doi.org\/10.1038\/s41598-024-84504-y","journal-title":"Sci. Rep."},{"issue":"10","key":"1054_CR39","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1049\/icp.2024.3274","volume":"2024","author":"R Ranjbarzadeh","year":"2024","unstructured":"Ranjbarzadeh, R., Keles, A.I., Crane, M., Bendechache, M.: Comparative analysis of real-clinical MRI and BraTS datasets for brain tumor segmentation. IET Conf. Proc. 2024(10), 39\u201346 (2024). https:\/\/doi.org\/10.1049\/icp.2024.3274","journal-title":"IET Conf. Proc."},{"issue":"9","key":"1054_CR40","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering11090945","volume":"11","author":"S Anari","year":"2024","unstructured":"Anari, S.: Efficientunetvit: efficient breast tumor segmentation utilizing UNet architecture and pretrained vision transformer. Bioengineering 11(9), 945 (2024). https:\/\/doi.org\/10.3390\/bioengineering11090945","journal-title":"Bioengineering"},{"key":"1054_CR41","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s42044-024-00216-6","volume":"8","author":"M Kia","year":"2025","unstructured":"Kia, M., Sadeghi, S., Safarpour, H., Kamsari, M., Ghoushchi, S.J., Ranjbarzadeh, R.: Innovative fusion of VGG16, MobileNet, EfficientNet, AlexNet, and ResNet50 for MRI-based brain tumor identification. Iran. J. Comput. Sci. 8, 185\u2013215 (2025). https:\/\/doi.org\/10.1007\/s42044-024-00216-6","journal-title":"Iran. J. Comput. Sci."},{"issue":"4","key":"1054_CR42","doi-asserted-by":"publisher","first-page":"499","DOI":"10.3390\/biomed4040038","volume":"4","author":"NT Sarshar","year":"2024","unstructured":"Sarshar, N.T., Sadeghi, S., Kamsari, M., Avazpour, M., Ghoushchi, S.J., Ranjbarzadeh, R.: Advancing brain MRI image classification: integrating VGG16 and ResNet50 with a multi-verse optimization method. BioMed 4(4), 499\u2013523 (2024). https:\/\/doi.org\/10.3390\/biomed4040038","journal-title":"BioMed"},{"key":"1054_CR43","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1016\/j.aej.2022.10.053","volume":"64","author":"GMM Alshmrani","year":"2023","unstructured":"Alshmrani, G.M.M., Ni, Q., Jiang, R., Pervaiz, H., Elshennawy, N.M.: A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images. Alex. Eng. J. 64, 923\u2013935 (2023). https:\/\/doi.org\/10.1016\/j.aej.2022.10.053","journal-title":"Alex. Eng. J."},{"issue":"4","key":"1054_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/diagnostics12040915","volume":"12","author":"S Kim","year":"2022","unstructured":"Kim, S., Rim, B., Choi, S., Lee, A., Min, S., Hong, M.: Deep learning in multi-class lung diseases\u2019 classification on chest X-ray images. Diagnostics 12(4), 1\u201324 (2022). https:\/\/doi.org\/10.3390\/diagnostics12040915","journal-title":"Diagnostics"},{"issue":"19","key":"1054_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/app11199289","volume":"11","author":"M Hong","year":"2021","unstructured":"Hong, M., Rim, B., Lee, H.C., Jang, H.U., Oh, J., Choi, S.: Multi-class classification of lung diseases using CNN models. Appl. Sci. (Switz.) 11(19), 1\u201317 (2021). https:\/\/doi.org\/10.3390\/app11199289","journal-title":"Appl. Sci. (Switz.)"},{"issue":"6","key":"1054_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/app11062751","volume":"11","author":"A Souid","year":"2021","unstructured":"Souid, A., Sakli, N., Sakli, H.: Classification and predictions of lung diseases from chest X-rays using MobileNetV2. Appl. Sci. (Switz.) 11(6), 1\u201316 (2021). https:\/\/doi.org\/10.3390\/app11062751","journal-title":"Appl. Sci. (Switz.)"},{"issue":"1","key":"1054_CR47","doi-asserted-by":"publisher","first-page":"199","DOI":"10.2991\/ijcis.d.201123.001","volume":"14","author":"W Wang","year":"2021","unstructured":"Wang, W., Liu, H., Li, J., Nie, H., Wang, X.: Using CFW-net deep learning models for X-ray images to detect COVID-19 patients. Int. J. Comput. Intell. Syst. 14(1), 199\u2013207 (2021). https:\/\/doi.org\/10.2991\/ijcis.d.201123.001","journal-title":"Int. J. Comput. Intell. Syst."},{"issue":"2","key":"1054_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/diagnostics13020248","volume":"13","author":"M Nawaz","year":"2023","unstructured":"Nawaz, M., Nazir, T., Baili, J., Khan, M.A., Kim, Y.J., Cha, J.H.: CXray-EffDet: chest disease detection and classification from X-ray images using the EfficientDet model. Diagnostics 13(2), 1\u201322 (2023). https:\/\/doi.org\/10.3390\/diagnostics13020248","journal-title":"Diagnostics"},{"key":"1054_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.126839","volume":"273","author":"W Xiong","year":"2025","unstructured":"Xiong, W., Zhang, G., Yan, D., Cao, L., Huang, X., Li, D.: Multichannel feature fusion network-based technique for heart sound signal classification and recognition. Expert Syst. Appl. 273, 126839 (2025). https:\/\/doi.org\/10.1016\/j.eswa.2025.126839","journal-title":"Expert Syst. Appl."},{"key":"1054_CR50","doi-asserted-by":"publisher","first-page":"10965","DOI":"10.1109\/TMM.2024.3428349","volume":"26","author":"W Song","year":"2024","unstructured":"Song, W., Wang, X., Guo, Y., Li, S., Xia, B., Hao, A.: Centerformer: a novel cluster center enhanced transformer for unconstrained dental plaque segmentation. IEEE Trans. Multimed. 26, 10965\u201310978 (2024). https:\/\/doi.org\/10.1109\/TMM.2024.3428349","journal-title":"IEEE Trans. Multimed."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-01054-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-01054-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-01054-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T09:52:50Z","timestamp":1765878770000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-01054-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,14]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1054"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-01054-5","relation":{},"ISSN":["1875-6883"],"issn-type":[{"type":"electronic","value":"1875-6883"}],"subject":[],"published":{"date-parts":[[2025,12,14]]},"assertion":[{"value":"14 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interests"}}],"article-number":"327"}}