{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T14:12:07Z","timestamp":1771251127037,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100019308","name":"Konya Technical University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100019308","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Brain tumor classification via MRI remains a critical challenge. This study presents a systematic evaluation of how preprocessing pipeline depth influences the performance of deep learning models, including both convolutional neural networks (CNNs) and Vision Transformer (ViT) architectures. A five-stage progressive preprocessing framework (denoising, contrast enhancement, edge sharpening, gamma correction, normalization) was designed and evaluated on a balanced MRI dataset of 8,000 images (glioma, meningioma, pituitary, normal). Comprehensive analysis revealed significant accuracy improvements (up to +\u20094.5%) with deeper preprocessing, especially for DenseNet121 and ViT Large. Stability analysis identified ViT Base R50 and VGG19 as the most robust architectures across varying preprocessing intensities. A composite clinical balance score, integrating performance, efficiency, and parameter load, ranked ViT Base R50 as the most suitable model for clinical deployment. This study emphasizes the pivotal role of preprocessing and proposes evidence-based guidelines for its design in clinical AI.<\/jats:p>","DOI":"10.1007\/s10586-026-05999-w","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T13:24:05Z","timestamp":1771248245000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comprehensive evaluation of preprocessing pipeline depth in deep learning-based brain tumor classification using CNN and vision transformer architectures"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5035-7575","authenticated-orcid":false,"given":"Fatma Z.","family":"Solak","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,16]]},"reference":[{"key":"5999_CR1","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1007\/s00401-016-1545-1","volume":"131","author":"DN Louis","year":"2016","unstructured":"Louis, D.N., et al.: The 2016 world health organization classification of tumors of the central nervous system: A summary. Acta Neuropathol. 131, 803\u2013820 (2016)","journal-title":"Acta Neuropathol."},{"issue":"8","key":"5999_CR2","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1016\/j.amjmed.2017.12.039","volume":"131","author":"JR McFaline-Figueroa","year":"2018","unstructured":"McFaline-Figueroa, J.R., Lee, E.Q.: Brain tumors. Am. J. Med. 131(8), 874\u2013882 (2018)","journal-title":"Am. J. Med."},{"key":"5999_CR3","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1023\/B:VLSI.0000028532.53893.82","volume":"38","author":"AM Reza","year":"2004","unstructured":"Reza, A.M.: Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J. VLSI signal. Process. Syst. signal. Image Video Technol. 38, 35\u201344 (2004)","journal-title":"J. VLSI signal. Process. Syst. signal. Image Video Technol."},{"key":"5999_CR4","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","volume":"30","author":"Z Akkus","year":"2017","unstructured":"Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., Erickson, B.J.: Deep learning for brain MRI segmentation: State of the Art and future directions. J. Digit. Imaging. 30, 449\u2013459 (2017)","journal-title":"J. Digit. Imaging"},{"key":"5999_CR5","doi-asserted-by":"publisher","first-page":"56","DOI":"10.3389\/fncom.2019.00056","volume":"13","author":"G Wang","year":"2019","unstructured":"Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty Estimation. Front. Comput. Neurosci. 13, 56 (2019)","journal-title":"Front. Comput. Neurosci."},{"key":"5999_CR6","unstructured":"Vaswani, A.: Attention is all you need, arXiv:1706.03762 (2017)"},{"issue":"9","key":"5999_CR7","doi-asserted-by":"publisher","first-page":"842","DOI":"10.3390\/jpm11090842","volume":"11","author":"SA Mali","year":"2021","unstructured":"Mali, S.A., et al.: Making radiomics more reproducible across scanner and imaging protocol variations: A review of harmonization methods. J. Personalized Med. 11(9), 842 (2021)","journal-title":"J. Personalized Med."},{"key":"5999_CR8","doi-asserted-by":"crossref","unstructured":"Yadav, A.C., Shah, K., Purohit, A., Kolekar, M.H.: Computer-aided diagnosis for multi-class classification of brain tumors using CNN features via transfer-learning. Multimedia Tools Appl. 84, 38959\u201338982 (2025)","DOI":"10.1007\/s11042-025-20751-z"},{"key":"5999_CR9","doi-asserted-by":"crossref","unstructured":"Yadav, A.C., Kolekar, M.H., Patil, D.B., Zope, M.K.: Image informatics for clinical and preclinical biomedical analysis. In: Dash, S., Pani, S.K., Santos, W.P.D., Chen, J.Y. (eds.) Mining Biomedical Text, Images and Visual Features for Information Retrieval, pp. 423\u2013460. Elsevier (2025)","DOI":"10.1016\/B978-0-443-15452-2.00020-0"},{"key":"5999_CR10","doi-asserted-by":"publisher","first-page":"111981","DOI":"10.1016\/j.knosys.2024.111981","volume":"299","author":"R \u0130ncir","year":"2024","unstructured":"\u0130ncir, R., Bozkurt, F.: Improving brain tumor classification with combined convolutional neural networks and transfer learning. Knowl. Based Syst. 299, 111981 (2024)","journal-title":"Knowl. Based Syst."},{"key":"5999_CR11","doi-asserted-by":"publisher","first-page":"101483","DOI":"10.1016\/j.imu.2024.101483","volume":"47","author":"MN Islam","year":"2024","unstructured":"Islam, M.N., Azam, M.S., Islam, M.S., Kanchan, M.H., Parvez, A.S., Islam, M.M.: An improved deep learning-based hybrid model with ensemble techniques for brain tumor detection from MRI image. Inf. Med. Unlocked. 47, 101483 (2024)","journal-title":"Inf. Med. Unlocked"},{"key":"5999_CR12","doi-asserted-by":"publisher","first-page":"1400341","DOI":"10.3389\/fonc.2024.1400341","volume":"14","author":"CKK Reddy","year":"2024","unstructured":"Reddy, C.K.K., et al.: A fine-tuned vision transformer based enhanced multi-class brain tumor classification using MRI scan imagery. Front. Oncol. 14, 1400341 (2024)","journal-title":"Front. Oncol."},{"issue":"1","key":"5999_CR13","doi-asserted-by":"publisher","first-page":"22797","DOI":"10.1038\/s41598-024-71893-3","volume":"14","author":"MM Ahmed","year":"2024","unstructured":"Ahmed, M.M., et al.: Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh. Sci. Rep. 14(1), 22797 (2024)","journal-title":"Sci. Rep."},{"key":"5999_CR14","doi-asserted-by":"publisher","first-page":"106117","DOI":"10.1016\/j.bspc.2024.106117","volume":"93","author":"S Khoramipour","year":"2024","unstructured":"Khoramipour, S., Gandomkar, M., Shakiba, M.: Enhancement of brain tumor classification from MRI images using multi-path convolutional neural network with SVM classifier. Biomed. Signal Process. Control. 93, 106117 (2024)","journal-title":"Biomed. Signal Process. Control"},{"issue":"7","key":"5999_CR15","doi-asserted-by":"publisher","first-page":"20487","DOI":"10.1007\/s11042-023-16143-w","volume":"83","author":"IS Rajput","year":"2024","unstructured":"Rajput, I.S., Gupta, A., Jain, V., Tyagi, S.: A transfer learning-based brain tumor classification using magnetic resonance images. Multimedia Tools Appl. 83(7), 20487\u201320506 (2024)","journal-title":"Multimedia Tools Appl."},{"issue":"35","key":"5999_CR16","doi-asserted-by":"publisher","first-page":"82719","DOI":"10.1007\/s11042-024-18780-1","volume":"83","author":"N Remzan","year":"2024","unstructured":"Remzan, N., Tahiry, K., Farchi, A.: Advancing brain tumor classification accuracy through deep learning: Harnessing radimagenet pre-trained convolutional neural networks, ensemble learning, and machine learning classifiers on MRI brain images. Multimedia Tools Appl. 83(35), 82719\u201382747 (2024)","journal-title":"Multimedia Tools Appl."},{"issue":"11","key":"5999_CR17","doi-asserted-by":"publisher","first-page":"33753","DOI":"10.1007\/s11042-023-16708-9","volume":"83","author":"S Mandloi","year":"2024","unstructured":"Mandloi, S., Zuber, M., Gupta, R.K.: An explainable brain tumor detection and classification model using deep learning and layer-wise relevance propagation. Multimedia Tools Appl. 83(11), 33753\u201333783 (2024)","journal-title":"Multimedia Tools Appl."},{"key":"5999_CR18","doi-asserted-by":"crossref","unstructured":"Sarker, S.: Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh, in 6th International Conference on Sustainable Technologies for Industry 5.0 (STI), 2024, pp. 1\u20136: IEEE. (2024)","DOI":"10.1109\/STI64222.2024.10951092"},{"key":"5999_CR19","doi-asserted-by":"publisher","first-page":"105778","DOI":"10.1016\/j.bspc.2023.105778","volume":"89","author":"B Sandhiya","year":"2024","unstructured":"Sandhiya, B., Raja, S.K.S.: Deep learning and optimized learning machine for brain tumor classification. Biomed. Signal Process. Control. 89, 105778 (2024)","journal-title":"Biomed. Signal Process. Control"},{"issue":"3","key":"5999_CR20","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1007\/s00521-024-10401-0","volume":"37","author":"KM Hosny","year":"2025","unstructured":"Hosny, K.M., Mohammed, M.A., Salama, R.A., Elshewey, A.M.: Explainable ensemble deep learning-based model for brain tumor detection and classification. Neural Comput. Appl. 37(3), 1289\u20131306 (2025)","journal-title":"Neural Comput. Appl."},{"issue":"3","key":"5999_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11227-025-06964-x","volume":"81","author":"MS Ba\u015farslan","year":"2025","unstructured":"Ba\u015farslan, M.S.: MC &M-BL: A novel classification model for brain tumor classification: multi-CNN and multi-BiLSTM. J. Supercomputing. 81(3), 1\u201325 (2025)","journal-title":"J. Supercomputing"},{"key":"5999_CR22","doi-asserted-by":"crossref","unstructured":"Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No new-net, in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, Revised Selected Papers, Part II 4, 2019, pp. 234\u2013244: Springer. (2018)","DOI":"10.1007\/978-3-030-11726-9_21"},{"key":"5999_CR23","unstructured":"Sartaj, A.K., Bhuvaji, Prajakta Bhumkar,Sameer Dedge and Swati Kanchan:. Brain Tumor Classification (MRI). (2020). Available: https:\/\/www.kaggle.com\/dsv\/1183165"},{"key":"5999_CR24","unstructured":"Zisserman, K.S.A.: Very Deep Convolutional Networks for Large-Scale Image Recognition, in International Conference on Learning Representations, (2015)"},{"issue":"23","key":"5999_CR25","doi-asserted-by":"publisher","first-page":"11185","DOI":"10.3390\/app112311185","volume":"11","author":"Z-P Jiang","year":"2021","unstructured":"Jiang, Z.-P., Liu, Y.-Y., Shao, Z.-E., Huang, K.-W.: An improved VGG16 model for pneumonia image classification. Appl. Sci. 11(23), 11185 (2021)","journal-title":"Appl. Sci."},{"issue":"12","key":"5999_CR26","doi-asserted-by":"publisher","first-page":"2208","DOI":"10.3390\/diagnostics11122208","volume":"11","author":"M.A. Khan","year":"2021","unstructured":"Khan, M.A., et al.: VGG19 network assisted joint segmentation and classification of lung nodules in CT images. Diagnostics. 11(12), 2208 (2021)","journal-title":"Diagnostics"},{"key":"5999_CR27","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, pp. 770\u2013778. (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5999_CR28","doi-asserted-by":"crossref","unstructured":"Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. nature. 542(7639) 115\u2013118 (2017)","DOI":"10.1038\/nature21056"},{"key":"5999_CR29","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700\u20134708. (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"5999_CR30","doi-asserted-by":"publisher","first-page":"103647","DOI":"10.1016\/j.bspc.2022.103647","volume":"76","author":"N Cinar","year":"2022","unstructured":"Cinar, N., Ozcan, A., Kaya, M.: A hybrid DenseNet121-UNet model for brain tumor segmentation from MR images. Biomed. Signal Process. Control. 76, 103647 (2022)","journal-title":"Biomed. Signal Process. Control"},{"key":"5999_CR31","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale, arXiv:2010.11929, (2020)"},{"key":"5999_CR32","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., 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":"5999_CR33","unstructured":"Chen, J., et al.: Transunet: Transformers make strong encoders for medical image segmentation, arXiv:2102.04306, (2021)"},{"issue":"9","key":"5999_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11760-025-04268-4","volume":"19","author":"A Solak","year":"2025","unstructured":"Solak, A.: Ensemble-based hybrid deep learning for Monkeypox detection: Merging instance-normalized Transformers with CNNs for enhanced diagnostic precision. Signal. Image Video Process. 19(9), 1\u201314 (2025)","journal-title":"Signal. Image Video Process."},{"key":"5999_CR35","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization, arXiv:1711.05101, (2017)"},{"key":"5999_CR36","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks, in Advances in neural information processing systems, pp. 1097\u20131105. (2012)"},{"key":"5999_CR37","doi-asserted-by":"crossref","unstructured":"Bisong, E.: Google colaboratory. In: McDermott, S., Fernando, R. (eds.) Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners, pp. 59\u201364. Springer (2019)","DOI":"10.1007\/978-1-4842-4470-8_7"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-026-05999-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-026-05999-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-026-05999-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T13:24:09Z","timestamp":1771248249000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-026-05999-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,16]]},"references-count":37,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["5999"],"URL":"https:\/\/doi.org\/10.1007\/s10586-026-05999-w","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,16]]},"assertion":[{"value":"6 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 February 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2026","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":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics declaration"}},{"value":"The author declares that no funds, grants, or other financial support were received during the preparation of this manuscript. The author has no relevant financial or non-financial interests to disclose.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Financial statement"}}],"article-number":"155"}}