{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T07:59:35Z","timestamp":1771747175164,"version":"3.50.1"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:00:00Z","timestamp":1749772800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:00:00Z","timestamp":1749772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Manipal University Jaipur"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Computing"],"DOI":"10.1007\/s10791-025-09637-8","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T10:24:35Z","timestamp":1749810275000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Enhancing medical diagnosis on chest X-rays: knowledge distillation from self-supervised based model to compressed student model"],"prefix":"10.1007","volume":"28","author":[{"given":"Jaydeep","family":"Kishore","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akshita","family":"Jain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Krishna Koushika","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pawan Kumar","family":"Mishra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shekhar","family":"Karanwal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Surendra","family":"Solanki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,13]]},"reference":[{"key":"9637_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102125","volume":"72","author":"E \u00c7all\u0131","year":"2021","unstructured":"\u00c7all\u0131 E, Sogancioglu E, van Ginneken B, van Leeuwen KG, Murphy K. Deep learning for chest X-ray analysis: a survey. Med Image Anal. 2021;72: 102125.","journal-title":"Med Image Anal"},{"key":"9637_CR2","doi-asserted-by":"crossref","unstructured":"Rehman A, Butt MA, Zaman M. A survey of medical image analysis using deep learning approaches. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 2021;1334\u20131342. IEEE.","DOI":"10.1109\/ICCMC51019.2021.9418385"},{"issue":"3","key":"9637_CR3","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1109\/TCBB.2019.2911947","volume":"18","author":"Y Wang","year":"2019","unstructured":"Wang Y, Sun L, Jin Q. Enhanced diagnosis of pneumothorax with an improved real-time augmentation for imbalanced chest X-rays data based on dcnn. IEEE\/ACM Trans Comput Biol Bioinf. 2019;18(3):951\u201362.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"5","key":"9637_CR4","doi-asserted-by":"publisher","first-page":"2605","DOI":"10.1109\/TCBB.2021.3066331","volume":"19","author":"J Zhou","year":"2021","unstructured":"Zhou J, Jing B, Wang Z, Xin H, Tong H. Soda: detecting covid-19 in chest X-rays with semi-supervised open set domain adaptation. IEEE\/ACM Trans Comput Biol Bioinf. 2021;19(5):2605\u201312.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"6","key":"9637_CR5","doi-asserted-by":"publisher","first-page":"2775","DOI":"10.1109\/TCBB.2021.3065361","volume":"18","author":"Y Song","year":"2021","unstructured":"Song Y, Zheng S, Li L, Zhang X, Zhang X, Huang Z, Chen J, Wang R, Zhao H, Chong Y, et al. Deep learning enables accurate diagnosis of novel coronavirus (covid-19) with ct images. IEEE\/ACM Trans Comput Biol Bioinf. 2021;18(6):2775\u201380.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"4","key":"9637_CR6","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1109\/TCBB.2020.3009859","volume":"18","author":"Y Pathak","year":"2020","unstructured":"Pathak Y, Shukla PK, Arya K. Deep bidirectional classification model for covid-19 disease infected patients. IEEE\/ACM Trans Comput Biol Bioinf. 2020;18(4):1234\u201341.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"21","key":"9637_CR7","doi-asserted-by":"publisher","first-page":"15884","DOI":"10.1109\/JIOT.2021.3056185","volume":"8","author":"W Zhang","year":"2021","unstructured":"Zhang W, Zhou T, Lu Q, Wang X, Zhu C, Sun H, Wang Z, Lo SK, Wang F-Y. Dynamic-fusion-based federated learning for covid-19 detection. IEEE Internet Things J. 2021;8(21):15884\u201391.","journal-title":"IEEE Internet Things J"},{"key":"9637_CR8","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017;4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"9637_CR9","doi-asserted-by":"crossref","unstructured":"Ferreira JR, Cardenas DAC, Moreno RA, de S\u00e1\u00a0Rebelo MDF, Krieger JE, Gutierrez MA. Multi-view ensemble convolutional neural network to improve classification of pneumonia in low contrast chest x-ray images. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020;1238\u20131241. IEEE.","DOI":"10.1109\/EMBC44109.2020.9176517"},{"issue":"1","key":"9637_CR10","first-page":"8862089","volume":"2021","author":"M Masud","year":"2021","unstructured":"Masud M, Bairagi AK, Nahid A-A, Sikder N, Rubaiee S, Ahmed A, Anand D. A pneumonia diagnosis scheme based on hybrid features extracted from chest radiographs using an ensemble learning algorithm. J Healthc Eng. 2021;2021(1):8862089.","journal-title":"J Healthc Eng"},{"issue":"1","key":"9637_CR11","first-page":"861","volume":"16","author":"H Gm","year":"2021","unstructured":"Gm H, Gourisaria MK, Rautaray SS, Pandey M. Pneumonia detection using cnn through chest x-ray. J Eng Sci Technol (JESTEC). 2021;16(1):861\u201376.","journal-title":"J Eng Sci Technol (JESTEC)"},{"key":"9637_CR12","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.procs.2023.01.018","volume":"218","author":"S Sharma","year":"2023","unstructured":"Sharma S, Guleria K. A deep learning based model for the detection of pneumonia from chest X-ray images using vgg-16 and neural networks. Proc Comput Sci. 2023;218:357\u201366.","journal-title":"Proc Comput Sci"},{"issue":"4","key":"9637_CR13","doi-asserted-by":"publisher","first-page":"390","DOI":"10.3390\/diagnostics14040390","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. 2024;14(4):390.","journal-title":"Diagnostics"},{"key":"9637_CR14","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou J, Yu B, Maybank SJ, Tao D. Knowledge distillation: a survey. Int J Comput Vision. 2021;129:1789\u2013819.","journal-title":"Int J Comput Vision"},{"key":"9637_CR15","doi-asserted-by":"publisher","first-page":"474","DOI":"10.7717\/peerj-cs.474","volume":"7","author":"A Alkhulaifi","year":"2021","unstructured":"Alkhulaifi A, Alsahli F, Ahmad I. Knowledge distillation in deep learning and its applications. PeerJ Comput Sci. 2021;7:474.","journal-title":"PeerJ Comput Sci"},{"key":"9637_CR16","doi-asserted-by":"crossref","unstructured":"Wang L, Yoon K-J. Knowledge distillation and student\u2013teacher learning for visual intelligence: a review and new outlooks. IEEE Trans Pattern Anal Mach Intell. 2021.","DOI":"10.1109\/TPAMI.2021.3055564"},{"key":"9637_CR17","doi-asserted-by":"publisher","first-page":"160749","DOI":"10.1109\/ACCESS.2020.3020802","volume":"8","author":"TKK Ho","year":"2020","unstructured":"Ho TKK, Gwak J. Utilizing knowledge distillation in deep learning for classification of chest x-ray abnormalities. IEEE Access. 2020;8:160749\u201361.","journal-title":"IEEE Access"},{"key":"9637_CR18","doi-asserted-by":"crossref","unstructured":"Van Sonsbeek T, Zhen X, Worring M, Shao L. Variational knowledge distillation for disease classification in chest x-rays. In: Information Processing in Medical Imaging: 27th International Conference, IPMI 2021, Virtual Event, June 28\u2013June 30, 2021, Proceedings 27, 2021;334\u2013345. Springer.","DOI":"10.1007\/978-3-030-78191-0_26"},{"issue":"4","key":"9637_CR19","doi-asserted-by":"publisher","first-page":"2455","DOI":"10.1109\/TCSVT.2021.3079900","volume":"32","author":"B Chen","year":"2021","unstructured":"Chen B, Zhang Z, Li Y, Lu G, Zhang D. Multi-label chest x-ray image classification via semantic similarity graph embedding. IEEE Trans Circuits Syst Video Technol. 2021;32(4):2455\u201368.","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"9637_CR20","doi-asserted-by":"crossref","unstructured":"Kishore J, Mukherjee S. Impact of autotuned fully connected layers on performance of self-supervised models for image classification. Mach Intell Res. 2024;1\u201313.","DOI":"10.1007\/s11633-023-1435-7"},{"issue":"19","key":"9637_CR21","doi-asserted-by":"publisher","first-page":"22086","DOI":"10.1007\/s10489-023-04598-1","volume":"53","author":"J Kishore","year":"2023","unstructured":"Kishore J, Mukherjee S. Auto cnn classifier based on knowledge transferred from self-supervised model. Appl Intell. 2023;53(19):22086\u2013104.","journal-title":"Appl Intell"},{"issue":"4","key":"9637_CR22","doi-asserted-by":"publisher","first-page":"1635","DOI":"10.1109\/TAI.2023.3322394","volume":"5","author":"J Kishore","year":"2023","unstructured":"Kishore J, Mukherjee S. Minimizing parameter overhead in self-supervised models for target task. IEEE Trans Artif Intell. 2023;5(4):1635\u201346.","journal-title":"IEEE Trans Artif Intell"},{"issue":"3","key":"9637_CR23","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/s13748-024-00329-w","volume":"13","author":"J Kishore","year":"2024","unstructured":"Kishore J, Mukherjee S. An ensemble of self-supervised teachers for minimal student model with auto-tuned hyperparameters via improved bayesian optimization. Progr Artif Intell. 2024;13(3):201\u201315.","journal-title":"Progr Artif Intell"},{"key":"9637_CR24","first-page":"9912","volume":"33","author":"M Caron","year":"2020","unstructured":"Caron M, Misra I, Mairal J, Goyal P, Bojanowski P, Joulin A. Unsupervised learning of visual features by contrasting cluster assignments. Adv Neural Inf Process Syst. 2020;33:9912\u201324.","journal-title":"Adv Neural Inf Process Syst"},{"issue":"5","key":"9637_CR25","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, McKeown A, Yang G, Wu X, Yan F, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172(5):1122\u201331.","journal-title":"Cell"},{"key":"9637_CR26","doi-asserted-by":"crossref","unstructured":"Hayat M, Ahmad N, Nasir A, Tariq ZA. Hybrid deep learning efficientnetv2 and vision transformer (effnetv2-vit) model for breast cancer histopathological image classification. IEEE Access 2024.","DOI":"10.1109\/ACCESS.2024.3503413"},{"issue":"10","key":"9637_CR27","doi-asserted-by":"publisher","first-page":"1614","DOI":"10.3390\/electronics11101614","volume":"11","author":"Z Ren","year":"2022","unstructured":"Ren Z, Zhang Y, Wang S. A hybrid framework for lung cancer classification. Electronics. 2022;11(10):1614.","journal-title":"Electronics"},{"key":"9637_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.fraope.2024.100170","volume":"8","author":"M Hayat","year":"2024","unstructured":"Hayat M. Squeeze & excitation joint with combined channel and spatial attention for pathology image super-resolution. Franklin Open. 2024;8: 100170.","journal-title":"Franklin Open"},{"issue":"3","key":"9637_CR29","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1049\/cit2.12216","volume":"8","author":"Z Ren","year":"2023","unstructured":"Ren Z, Wang S, Zhang Y. Weakly supervised machine learning. CAAI Trans Intell Technol. 2023;8(3):549\u201380.","journal-title":"CAAI Trans Intell Technol"},{"key":"9637_CR30","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1016\/j.csbj.2024.04.004","volume":"23","author":"Z Ren","year":"2024","unstructured":"Ren Z, Lan Q, Zhang Y, Wang S. Exploring simple triplet representation learning. Comput Struct Biotechnol J. 2024;23:1510\u201321.","journal-title":"Comput Struct Biotechnol J"},{"key":"9637_CR31","unstructured":"Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz C, Shpanskaya K, et al. Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 2017."},{"key":"9637_CR32","doi-asserted-by":"crossref","unstructured":"Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. Chest x-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017;2097\u20132106.","DOI":"10.1109\/CVPR.2017.369"},{"key":"9637_CR33","first-page":"590","volume":"33","author":"J Irvin","year":"2019","unstructured":"Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, Marklund H, Haghgoo B, Ball R, Shpanskaya K, et al. Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison. Proc AAAI Conf Artif Intell. 2019;33:590\u20137.","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"9","key":"9637_CR34","doi-asserted-by":"publisher","first-page":"0256630","DOI":"10.1371\/journal.pone.0256630","volume":"16","author":"R Kundu","year":"2021","unstructured":"Kundu R, Das R, Geem ZW, Han G-T, Sarkar R. Pneumonia detection in chest x-ray images using an ensemble of deep learning models. PLoS ONE. 2021;16(9):0256630.","journal-title":"PLoS ONE"},{"issue":"5","key":"9637_CR35","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.3390\/diagnostics12051280","volume":"12","author":"M Mujahid","year":"2022","unstructured":"Mujahid M, Rustam F, \u00c1lvarez R, Luis Vidal Maz\u00f3n J, D\u00edez IDIT, Ashraf I. Pneumonia classification from x-ray images with inception-v3 and convolutional neural network. Diagnostics. 2022;12(5):1280.","journal-title":"Diagnostics"},{"key":"9637_CR36","doi-asserted-by":"crossref","unstructured":"Sharma S, Guleria K. A deep learning model for early prediction of pneumonia using vgg19 and neural networks. In: Mobile Radio Communications and 5G Networks: Proceedings of Third MRCN 2022, pp. 597\u2013612. Springer 2023.","DOI":"10.1007\/978-981-19-7982-8_50"},{"issue":"6","key":"9637_CR37","first-page":"1264","volume":"41","author":"Y Demir","year":"2023","unstructured":"Demir Y, Bing\u00f6l \u00d6. Detection of pneumonia from pediatric chest x-ray images by transfer learning. Sigma J Eng Nat Sci. 2023;41(6):1264\u201371.","journal-title":"Sigma J Eng Nat Sci"},{"key":"9637_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105857","volume":"90","author":"AM Rifai","year":"2024","unstructured":"Rifai AM, Raharjo S, Utami E, Ariatmanto D. Analysis for diagnosis of pneumonia symptoms using chest x-ray based on mobilenetv2 models with image enhancement using white balance and contrast limited adaptive histogram equalization (clahe). Biomed Signal Process Control. 2024;90: 105857.","journal-title":"Biomed Signal Process Control"},{"key":"9637_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106416","volume":"123","author":"C Asswin","year":"2023","unstructured":"Asswin C, Ks DK, Dora A, Ravi V, Sowmya V, Gopalakrishnan E, Soman K, et al. Transfer learning approach for pediatric pneumonia diagnosis using channel attention deep cnn architectures. Eng Appl Artif Intell. 2023;123: 106416.","journal-title":"Eng Appl Artif Intell"},{"key":"9637_CR40","doi-asserted-by":"crossref","unstructured":"Ali M, Shahroz M, Akram U, Mushtaq MF, Altamiranda SC, Obregon SA, D\u00edez IDLT, Ashraf I. Pneumonia detection using chest radiographs with novel efficientnetv2l model. IEEE Access 2024.","DOI":"10.1109\/ACCESS.2024.3372588"},{"issue":"1","key":"9637_CR41","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1186\/s12880-023-01174-4","volume":"23","author":"K Guo","year":"2023","unstructured":"Guo K, Cheng J, Li K, Wang L, Lv Y, Cao D. Diagnosis and detection of pneumonia using weak-label based on x-ray images: a multi-center study. BMC Med Imaging. 2023;23(1):209.","journal-title":"BMC Med Imaging"},{"key":"9637_CR42","doi-asserted-by":"crossref","unstructured":"Gui J, Chen T, Zhang J, Cao Q, Sun Z, Luo H, Tao D. A survey on self-supervised learning: algorithms, applications, and future trends. IEEE Trans Pattern Anal Mach Intell. 2024.","DOI":"10.1109\/TPAMI.2024.3415112"},{"key":"9637_CR43","doi-asserted-by":"crossref","unstructured":"Liu X, Zhang F, Hou Z, Mian L, Wang Z, Zhang J, Tang J. Self-supervised learning: generative or contrastive. IEEE Trans Knowl Data Eng. 2021.","DOI":"10.1109\/TKDE.2021.3090866"},{"key":"9637_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102879","volume":"89","author":"C Zhang","year":"2023","unstructured":"Zhang C, Zheng H, Gu Y. Dive into the details of self-supervised learning for medical image analysis. Med Image Anal. 2023;89: 102879.","journal-title":"Med Image Anal"},{"issue":"3","key":"9637_CR45","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1007\/s10278-023-00782-4","volume":"36","author":"K Cho","year":"2023","unstructured":"Cho K, Kim KD, Nam Y, Jeong J, Kim J, Choi C, Lee S, Lee JS, Woo S, Hong G-S, et al. Chess: chest x-ray pre-trained model via self-supervised contrastive learning. J Digit Imaging. 2023;36(3):902\u201310.","journal-title":"J Digit Imaging"},{"issue":"1","key":"9637_CR46","doi-asserted-by":"publisher","first-page":"19518","DOI":"10.1038\/s41598-023-46345-z","volume":"13","author":"W Celniak","year":"2023","unstructured":"Celniak W, Wodzi\u0144ski M, Jurgas A, Burti S, Zotti A, Atzori M, M\u00fcller H, Banzato T. Improving the classification of veterinary thoracic radiographs through inter-species and inter-pathology self-supervised pre-training of deep learning models. Sci Rep. 2023;13(1):19518.","journal-title":"Sci Rep"},{"issue":"4","key":"9637_CR47","first-page":"1618","volume":"37","author":"K Imagawa","year":"2024","unstructured":"Imagawa K, Shiomoto K. Evaluation of effectiveness of self-supervised learning in chest x-ray imaging to reduce annotated images. J Imag Inf Med. 2024;37(4):1618\u201324.","journal-title":"J Imag Inf Med"},{"key":"9637_CR48","doi-asserted-by":"crossref","unstructured":"Kim J, Chang S, Kwak N. Pqk: model compression via pruning, quantization, and knowledge distillation. arXiv preprint arXiv:2106.14681 2021.","DOI":"10.21437\/Interspeech.2021-248"},{"key":"9637_CR49","unstructured":"Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 2015."},{"issue":"5","key":"9637_CR50","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J. Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging. 2016;35(5):1299\u2013312.","journal-title":"IEEE Trans Med Imaging"},{"key":"9637_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2020.109745","volume":"422","author":"R Li","year":"2020","unstructured":"Li R, Yang H, Yang C. Parallel multilevel restricted schwarz preconditioners for implicit simulation of subsurface flows with peng-robinson equation of state. J Comput Phys. 2020;422: 109745.","journal-title":"J Comput Phys"},{"key":"9637_CR52","first-page":"10824","volume":"35","author":"J Zeng","year":"2021","unstructured":"Zeng J, Xie P. Contrastive self-supervised learning for graph classification. Proc AAAI Conf Artif Intell. 2021;35:10824\u201332.","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"8","key":"9637_CR53","doi-asserted-by":"publisher","first-page":"2292","DOI":"10.1109\/JBHI.2020.2967084","volume":"24","author":"B Chen","year":"2020","unstructured":"Chen B, Li J, Lu G, Yu H, Zhang D. Label co-occurrence learning with graph convolutional networks for multi-label chest x-ray image classification. IEEE J Biomed Health Inform. 2020;24(8):2292\u2013302.","journal-title":"IEEE J Biomed Health Inform"},{"key":"9637_CR54","doi-asserted-by":"crossref","unstructured":"Termritthikun C, Umer A, Suwanwimolkul S, Xia F, Lee I. Explainable knowledge distillation for on-device chest x-ray classification. In: IEEE\/ACM Transactions on Computational Biology and Bioinformatics 2023.","DOI":"10.1109\/TCBB.2023.3272333"},{"key":"9637_CR55","doi-asserted-by":"crossref","unstructured":"Asham MA, Al-Shargabi AA, Al-Sabri R, Meftah I. A lightweight deep learning model with knowledge distillation for pulmonary diseases detection in chest x-rays. Multimedia Tools Appl. 2024;1\u201329.","DOI":"10.1007\/s11042-024-19638-2"},{"key":"9637_CR56","doi-asserted-by":"crossref","unstructured":"Kabir MM, Mridha M, Rahman A, Hamid MA, Monowar MM. Detection of covid-19, pneumonia, and tuberculosis from radiographs using ai-driven knowledge distillation. Heliyon. 2024;10(5).","DOI":"10.1016\/j.heliyon.2024.e26801"},{"key":"9637_CR57","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016;770\u20138.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"9637_CR58","first-page":"4180949","volume":"2019","author":"O Stephen","year":"2019","unstructured":"Stephen O, Sain M, Maduh UJ, Jeong D-U. An efficient deep learning approach to pneumonia classification in healthcare. J Healthcare Eng. 2019;2019(1):4180949.","journal-title":"J Healthcare Eng"},{"key":"9637_CR59","unstructured":"To\u011fa\u00e7ar M, Ergen B, Sertkaya ME. Zat\u00fcrre hastal\u0131\u011f\u0131n\u0131n derin \u00f6\u011frenme modeli ile tespiti. Firat Univ J Eng Sci. 2019;31(1)."},{"issue":"3","key":"9637_CR60","doi-asserted-by":"publisher","first-page":"129","DOI":"10.31127\/tuje.652358","volume":"4","author":"OD G\u00fclg\u00fcn","year":"2020","unstructured":"G\u00fclg\u00fcn OD, Erol H. Classification performance comparisons of deep learning models in pneumonia diagnosis using chest x-ray images. Turkish J Eng. 2020;4(3):129\u201341.","journal-title":"Turkish J Eng"},{"key":"9637_CR61","unstructured":"Shah U, Abd-Alrazeq A, Alam T, Househ M, Shah Z. An efficient method to predict pneumonia from chest x-rays using deep learning approach. In: The Importance of Health Informatics in Public Health During a Pandemic, pp. 457\u2013460. IOS Press 2020."},{"key":"9637_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.06.023","volume":"187","author":"G Liang","year":"2020","unstructured":"Liang G, Zheng L. A transfer learning method with deep residual network for pediatric pneumonia diagnosis. Comput Methods Progr Biomed. 2020;187: 104964.","journal-title":"Comput Methods Progr Biomed"},{"key":"9637_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100360","volume":"19","author":"M Rahimzadeh","year":"2020","unstructured":"Rahimzadeh M, Attar A. 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. 2020;19: 100360.","journal-title":"Inf Med Unlocked"},{"key":"9637_CR64","doi-asserted-by":"crossref","unstructured":"Dem\u0131r Y, B\u0131ng\u00f6l \u00d6. Pneumonia detection from pediatric lung x-ray images with convolutional neural network method. In: 2021 29th Signal Processing and Communications Applications Conference (SIU), 2021;1\u20134. IEEE.","DOI":"10.1109\/SIU53274.2021.9477835"},{"key":"9637_CR65","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.neucom.2022.01.055","volume":"481","author":"C Ieracitano","year":"2022","unstructured":"Ieracitano C, Mammone N, Versaci M, Varone G, Ali A-R, Armentano A, Calabrese G, Ferrarelli A, Turano L, Tebala C, et al. A fuzzy-enhanced deep learning approach for early detection of covid-19 pneumonia from portable chest x-ray images. Neurocomputing. 2022;481:202\u201315.","journal-title":"Neurocomputing"},{"issue":"13","key":"9637_CR66","doi-asserted-by":"publisher","first-page":"6448","DOI":"10.3390\/app12136448","volume":"12","author":"A Mabrouk","year":"2022","unstructured":"Mabrouk A, Diaz Redondo RP, Dahou A, Abd Elaziz M, Kayed M. Pneumonia detection on chest x-ray images using ensemble of deep convolutional neural networks. Appl Sci. 2022;12(13):6448.","journal-title":"Appl Sci"},{"issue":"5","key":"9637_CR67","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/j.irbm.2021.12.004","volume":"43","author":"K Wang","year":"2022","unstructured":"Wang K, Jiang P, Meng J, Jiang X. Attention-based densenet for pneumonia classification. Irbm. 2022;43(5):479\u201385.","journal-title":"Irbm."},{"key":"9637_CR68","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2023.100176","volume":"3","author":"H Bhatt","year":"2023","unstructured":"Bhatt H, Shah M. A convolutional neural network ensemble model for pneumonia detection using chest x-ray images. Healthcare Anal. 2023;3: 100176.","journal-title":"Healthcare Anal"},{"issue":"4","key":"9637_CR69","doi-asserted-by":"publisher","first-page":"3239","DOI":"10.1007\/s12652-021-03464-7","volume":"14","author":"S Goyal","year":"2023","unstructured":"Goyal S, Singh R. Detection and classification of lung diseases for pneumonia and covid-19 using machine and deep learning techniques. J Ambient Intell Humaniz Comput. 2023;14(4):3239\u201359.","journal-title":"J Ambient Intell Humaniz Comput"}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09637-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-025-09637-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09637-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T22:02:15Z","timestamp":1749852135000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-025-09637-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,13]]},"references-count":69,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["9637"],"URL":"https:\/\/doi.org\/10.1007\/s10791-025-09637-8","relation":{},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,13]]},"assertion":[{"value":"17 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2025","order":3,"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 interest to disclose. Further, the authors certify that the research presented in this article does not involve any human participants or animals.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"118"}}