{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T12:45:23Z","timestamp":1765370723644,"version":"3.46.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T00:00:00Z","timestamp":1752451200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T00:00:00Z","timestamp":1752451200000},"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":["Iran J Comput Sci"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s42044-025-00301-4","type":"journal-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T19:32:11Z","timestamp":1752521531000},"page":"2049-2082","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An integrative Flamingo multitracker model capsule Vision Transformer for medical image-based disease diagnosis"],"prefix":"10.1007","volume":"8","author":[{"given":"Maheswari","family":"Gunasekaran","sequence":"first","affiliation":[]},{"given":"Gopalakrishnan","family":"Subburayalu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,14]]},"reference":[{"key":"301_CR1","doi-asserted-by":"crossref","unstructured":"Varshney, T., Sehrawat, R.: Object detection approach for pneumonia detection using X-ray images. In: Data-Driven Analytics for Healthcare. Apple Academic Press, pp. 55\u201371 (2025)","DOI":"10.1201\/9781003558743-4"},{"key":"301_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107268","volume":"102","author":"E Rajasekar","year":"2025","unstructured":"Rajasekar, E., Chandra, H., Pears, N., Vairavasundaram, S., Kotecha, K.: Lung image quality assessment and diagnosis using generative autoencoders in unsupervised ensemble learning. Biomed. Signal Process. Control 102, 107268 (2025)","journal-title":"Biomed. Signal Process. Control"},{"key":"301_CR3","doi-asserted-by":"crossref","unstructured":"Kaur, P., Kaur, S., Kaur, P.: Deep learning-based approaches for diagnosis and detection of osteoporosis using clinical data of CT and X-ray images. In: Computational Methods in Science and Technology. CRC Press, pp. 523\u2013528 (2025)","DOI":"10.1201\/9781003501244-79"},{"key":"301_CR4","doi-asserted-by":"crossref","unstructured":"Mahobiya, C., Iyer, S.S.: Machine learning for bone deformation detection in real-world applications. In: Diagnosing Musculoskeletal Conditions using Artifical Intelligence and Machine Learning to Aid Interpretation of Clinical Imaging. Elsevier, pp. 223\u2013242 (2025)","DOI":"10.1016\/B978-0-443-32892-3.00012-9"},{"key":"301_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107124","volume":"100","author":"B Cansiz","year":"2025","unstructured":"Cansiz, B., Kilinc, C.U., Serbes, G.: Deep learning-driven feature engineering for lung disease classification through electrical impedance tomography imaging. Biomed. Signal Process. Control 100, 107124 (2025)","journal-title":"Biomed. Signal Process. Control"},{"key":"301_CR6","doi-asserted-by":"crossref","unstructured":"Jaiswal, T., Dash, S.: Deep learning in medical image analysis. In: Mining Biomedical Text, Images and Visual Features for Information Retrieval. Elsevier, pp. 287\u2013295 (2025)","DOI":"10.1016\/B978-0-443-15452-2.00014-5"},{"key":"301_CR7","doi-asserted-by":"crossref","unstructured":"Aswiga, R.: Analysis of deep learning methodologies for handling non-medical big data and very limited medical data with feature extraction and annotation techniques. In: Machine Learning Hybridization and Optimization for Intelligent Applications. CRC Press, pp. 78\u2013107 (2025)","DOI":"10.1201\/9781003465775-6"},{"key":"301_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103376","volume":"99","author":"L Mei","year":"2025","unstructured":"Mei, L., Deng, K., Cui, Z., Fang, Y., Li, Y., Lai, H., et al.: Clinical knowledge-guided hybrid classification network for automatic periodontal disease diagnosis in X-ray image. Med. Image Anal. 99, 103376 (2025)","journal-title":"Med. Image Anal."},{"key":"301_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109577","volume":"185","author":"J Fu","year":"2025","unstructured":"Fu, J., Ouyang, A., Yang, J., Yang, D., Ge, G., Jin, H., et al.: SMDFnet: saliency multiscale dense fusion network for MRI and CT image fusion. Comput. Biol. Med. 185, 109577 (2025)","journal-title":"Comput. Biol. Med."},{"key":"301_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107252","volume":"102","author":"W Si","year":"2025","unstructured":"Si, W., Wang, G., Liu, L., Zhang, L., Qiao, L.: Graph neural network with modular attention for identifying brain disorders. Biomed. Signal Process. Control 102, 107252 (2025)","journal-title":"Biomed. Signal Process. Control"},{"key":"301_CR11","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1002\/jemt.24682","volume":"88","author":"K Priyadarshini","year":"2025","unstructured":"Priyadarshini, K., Ali, S.A., Sivanandam, K., Alagarsamy, M.: Human lung cancer classification and comprehensive analysis using different machine learning techniques. Microsc. Res. Tech. 88, 234\u2013250 (2025)","journal-title":"Microsc. Res. Tech."},{"key":"301_CR12","first-page":"1","volume":"25","author":"EL Irede","year":"2024","unstructured":"Irede, E.L., Aworinde, O.R., Lekan, O.K., Amienghemhen, O.D., Okonkwo, T.P., Onivefu, A.P., et al.: Medical imaging: a critical review on X-ray imaging for the detection of infection. Biomed. Mater. Dev. 25, 1\u201345 (2024)","journal-title":"Biomed. Mater. Dev."},{"key":"301_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10420150.2024.2338368","volume":"179","author":"M-C Chiu","year":"2024","unstructured":"Chiu, M.-C., Wei, C.-J., Li, J.-J.: Interpretable deep learning analysis on correction of motion blur X-ray images to ensure the efficiency and reliability of clinical decisions. Radiat. Effects Defects Solids 179, 1\u201320 (2024)","journal-title":"Radiat. Effects Defects Solids"},{"key":"301_CR14","first-page":"1","volume":"17","author":"G Maheswari","year":"2024","unstructured":"Maheswari, G., Gopalakrishnan, S.: A smart multimodal framework based on squeeze excitation capsule network (SECNet) model for disease diagnosis using dissimilar medical images. Int. J. Inf. Technol. 17, 1\u201319 (2024)","journal-title":"Int. J. Inf. Technol."},{"key":"301_CR15","doi-asserted-by":"crossref","unstructured":"Isukapalli, V.K., Kumar, R.D., Gopalakrishnan, S.: Image-based bone marrow malignancy detection: motivation, challenges and recommendations. In: 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1\u20136 (2024)","DOI":"10.1109\/ICCCNT61001.2024.10723965"},{"key":"301_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodcont.2023.110092","volume":"155","author":"A Tempelaere","year":"2024","unstructured":"Tempelaere, A., Van Doorselaer, L., He, J., Verboven, P., Nicolai, B.M.: BraeNet: Internal disorder detection in \u2018Braeburn\u2019 apple using X-ray imaging data. Food Control 155, 110092 (2024)","journal-title":"Food Control"},{"key":"301_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-024-18789-6","volume":"83","author":"S Goyal","year":"2024","unstructured":"Goyal, S., Singh, R.: DIFDD: deep intelligence framework for disease detection using patients electrocardiogram signals and X-ray images. Multimedia Tools Appl. 83, 1\u201330 (2024)","journal-title":"Multimedia Tools Appl."},{"key":"301_CR18","doi-asserted-by":"crossref","unstructured":"Degadwala, S., Krishnamurthy, V.N.D., Vyas, D.: DeepSpine: multi-class spine X-ray conditions classification using deep learning. In: 2024 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL), pp. 8\u201313 (2024)","DOI":"10.1109\/ICSADL61749.2024.00008"},{"key":"301_CR19","first-page":"1","volume":"16","author":"MM Gomaa","year":"2024","unstructured":"Gomaa, M.M., Zainelabdeen, A.G., Elnashar, A., Zaki, A.M.: Brain tumor X-ray images enhancement and classification using anisotropic diffusion filter and transfer learning models. Int. J. Inf. Technol. 16, 1\u20139 (2024)","journal-title":"Int. J. Inf. Technol."},{"key":"301_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126946","volume":"566","author":"L-W Cheng","year":"2024","unstructured":"Cheng, L.-W., Chou, H.-H., Cai, Y.-X., Huang, K.-Y., Hsieh, C.-C., Chu, P.-L., et al.: Automated detection of vertebral fractures from X-ray images: a novel machine learning model and survey of the field. Neurocomputing 566, 126946 (2024)","journal-title":"Neurocomputing"},{"key":"301_CR21","doi-asserted-by":"crossref","unstructured":"Juneja, M., Saini, S.K., Kaur, H., Aggarwal, N.: Machine learning techniques in computer-aided diagnosis for effective detection of malignant tissues. In: Handbook of Oncobiology: From Basic to Clinical Sciences. Springer, pp. 739\u2013756 (2024)","DOI":"10.1007\/978-981-99-6263-1_34"},{"key":"301_CR22","first-page":"223","volume":"4","author":"SR Gayam","year":"2024","unstructured":"Gayam, S.R.: Deep learning for image recognition: advanced algorithms and applications in medical imaging, autonomous vehicles, and security systems. Hong Kong J. AI Med. 4, 223\u2013258 (2024)","journal-title":"Hong Kong J. AI Med."},{"key":"301_CR23","doi-asserted-by":"publisher","first-page":"43035","DOI":"10.1007\/s11042-023-17326-1","volume":"83","author":"B Abhisheka","year":"2024","unstructured":"Abhisheka, B., Biswas, S.K., Purkayastha, B., Das, D., Escargueil, A.: Recent trend in medical imaging modalities and their applications in disease diagnosis: a review. Multimedia Tools Appl. 83, 43035\u201343070 (2024)","journal-title":"Multimedia Tools Appl."},{"key":"301_CR24","first-page":"36","volume":"2","author":"Q Zeng","year":"2024","unstructured":"Zeng, Q., Sun, W., Xu, J., Wan, W., Pan, L.: Machine learning-based medical imaging detection and diagnostic assistance. Int. J. Comput. Sci. Inf. Technol. 2, 36\u201344 (2024)","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"key":"301_CR25","first-page":"1","volume":"87","author":"MA Al-qaness","year":"2024","unstructured":"Al-qaness, M.A., Zhu, J., Al-Alimi, D., Dahou, A., Alsamhi, S.H., Abd Elaziz, M., et al.: Chest X-ray images for lung disease detection using deep learning techniques: a comprehensive survey. Arch. Comput. Methods Eng. 87, 1\u201335 (2024)","journal-title":"Arch. Comput. Methods Eng."},{"key":"301_CR26","first-page":"1","volume":"15","author":"X Li","year":"2024","unstructured":"Li, X., Zhang, L., Yang, J., Teng, F.: Role of artificial intelligence in medical image analysis: a review of current trends and future directions. J. Med. Biol. Eng. 15, 1\u201313 (2024)","journal-title":"J. Med. Biol. Eng."},{"key":"301_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2023.102320","volume":"111","author":"MY Ouis","year":"2024","unstructured":"Ouis, M.Y., Akhloufi, M.A.: Deep learning for report generation on chest X-ray images. Comput. Med. Imaging Graph. 111, 102320 (2024)","journal-title":"Comput. Med. Imaging Graph."},{"key":"301_CR28","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1007\/s12596-022-01089-3","volume":"53","author":"W El-Shafai","year":"2024","unstructured":"El-Shafai, W., Mahmoud, A.A., Ali, A.M., El-Rabaie, E.-S.M., Taha, T.E., El-Fishawy, A.S., et al.: Efficient classification of different medical image multimodalities based on simple CNN architecture and augmentation algorithms. J. Opt. 53, 775\u2013787 (2024)","journal-title":"J. Opt."},{"key":"301_CR29","first-page":"1","volume":"45","author":"ESD Buaka","year":"2024","unstructured":"Buaka, E.S.D., Moid, M.Z.I.: AI and medical imaging technology: evolution, impacts, and economic insights. J. Technol. Transf. 45, 1\u201313 (2024)","journal-title":"J. Technol. Transf."},{"key":"301_CR30","doi-asserted-by":"crossref","unstructured":"Ozaltin, O., Yeniay, O.: Computational intelligence on medical imaging with artificial neural networks. In: Mining Biomedical Text, Images and Visual Features for Information Retrieval. Elsevier, pp. 227\u2013257 (2025)","DOI":"10.1016\/B978-0-443-15452-2.00011-X"},{"key":"301_CR31","doi-asserted-by":"crossref","unstructured":"Poloju, N., Rajaram, A.: Hybrid technique for lung disease classification based on machine learning and optimization using X-ray images. Multimedia Tools Appl. 1\u201323 (2024)","DOI":"10.1007\/s11042-024-19959-2"},{"key":"301_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108910","volume":"179","author":"J Kaur","year":"2024","unstructured":"Kaur, J., Kaur, P.: A systematic literature analysis of multi-organ cancer diagnosis using deep learning techniques. Comput. Biol. Med. 179, 108910 (2024)","journal-title":"Comput. Biol. Med."},{"key":"301_CR33","doi-asserted-by":"crossref","unstructured":"Chakraborty, S., Pradhan, B.: Editorial for the special issue \u201cMachine learning in computer vision and image sensing: theory and applications\u201d, vol. 24. MDPI, p. 2874 (2024)","DOI":"10.3390\/s24092874"},{"key":"301_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, L., Qiao, Z., Li, L.: An evolutionary deep learning method based on improved heap-based optimization for medical image classification and diagnosis. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3433483"},{"key":"301_CR35","doi-asserted-by":"publisher","first-page":"3451","DOI":"10.3390\/app14083451","volume":"14","author":"G Duan","year":"2024","unstructured":"Duan, G., Zhang, S., Shang, Y., Kong, W.: Research on X-ray diagnosis model of musculoskeletal diseases based on deep learning. Appl. Sci. 14, 3451 (2024)","journal-title":"Appl. Sci."},{"key":"301_CR36","doi-asserted-by":"publisher","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, J.B., Lee, S.M., Kim, J.H., et al.: Multi-task vision transformer using low-level chest X-ray feature corpus for COVID-19 diagnosis and severity quantification. Med. Image Anal. 75, 102299 (2022)","journal-title":"Med. Image Anal."},{"key":"301_CR37","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D. P., Song, K., Liang, D., et al.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 568\u2013578 (2021)","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"301_CR38","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., et al.: UNetR: transformers for 3D medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 574\u2013584 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"301_CR39","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-UNeT: UNeT-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205\u2013218. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-25066-8_9"},{"issue":"1","key":"301_CR40","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1186\/s12859-023-05462-2","volume":"24","author":"N Ahmad","year":"2023","unstructured":"Ahmad, N., Strand, R., Sparres\u00e4ter, B., Tarai, S., Lundstr\u00f6m, E., Bergstr\u00f6m, G., et al.: Automatic segmentation of large-scale CT image datasets for detailed body composition analysis. BMC Bioinform. 24(1), 346 (2023)","journal-title":"BMC Bioinform."},{"key":"301_CR41","doi-asserted-by":"crossref","unstructured":"Hayat, M., Aramvith, S., Achakulvisut, T.: SEGSRNet for stereo-endoscopic image super-resolution and surgical instrument segmentation. In: 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1\u20134. IEEE (2024)","DOI":"10.1109\/EMBC53108.2024.10782794"},{"key":"301_CR42","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. Frankl. Open 8, 100170 (2024)","journal-title":"Frankl. Open"},{"key":"301_CR43","unstructured":"Kermany, D., Zhang, K., Goldbaum, M.: Chest X-ray Images (Pneumonia). Kaggle (2018). https:\/\/www.kaggle.com\/datasets\/paultimothymooney\/chest-xray-pneumonia"},{"key":"301_CR44","unstructured":"Paul Mooney: Lung CT Scan Images. Kaggle (2018). https:\/\/www.kaggle.com\/datasets\/andrewmvd\/lung-and-colon-cancer-histopathological-images"},{"key":"301_CR45","unstructured":"Utkarshtiwari: Brain Tumor MRI Dataset. Kaggle (2021). https:\/\/www.kaggle.com\/datasets\/utkarshsaxenadn\/brain-mri-images-for-brain-tumor-detection"}],"container-title":["Iran Journal of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42044-025-00301-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42044-025-00301-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42044-025-00301-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:09:58Z","timestamp":1765357798000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42044-025-00301-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,14]]},"references-count":45,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["301"],"URL":"https:\/\/doi.org\/10.1007\/s42044-025-00301-4","relation":{},"ISSN":["2520-8438","2520-8446"],"issn-type":[{"type":"print","value":"2520-8438"},{"type":"electronic","value":"2520-8446"}],"subject":[],"published":{"date-parts":[[2025,7,14]]},"assertion":[{"value":"11 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 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 declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All subjects gave their informed consent for inclusion before they participated in this study. This study was conducted by the Declaration of Helsinki. The corresponding author consents to participate in the research project and the following has been explained to her: the research may not be of direct benefit to her. Her participation is completely voluntary. She has the right to withdraw from the study at any time without any implications.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent"}}]}}