{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T19:12:38Z","timestamp":1778526758313,"version":"3.51.4"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T00:00:00Z","timestamp":1704240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T00:00:00Z","timestamp":1704240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"EAN"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Brain tumors can be generated anywhere in the brain, with an extensive size range and morphology that makes it challenging to identify and classify. Classifying brain tumors is essential for developing personalized treatment plans. Different types of brain tumors have different responses to treatment, and an accurate classification can help medical professionals develop treatment plans tailored to each patient\u2019s needs. Therefore, this case study aimed to classify T1-weighted contrast-enhanced images of three types of tumors through various approaches, from shallow neural networks to fine-tuning deep neural networks trained. Comparing shallow and deep neural network approaches could help to understand the trade-offs between their performance, interoperability, interpretability, benefits, limitations, scopes, and overall, choosing the best method for a given problem.<\/jats:p>","DOI":"10.1007\/s42979-023-02431-7","type":"journal-article","created":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T19:03:33Z","timestamp":1704308613000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Classification of Brain Tumors: A Comparative Approach of Shallow and Deep Neural Networks"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1146-5397","authenticated-orcid":false,"given":"Sebasti\u00e1n Felipe \u00c1lvarez","family":"Montoya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0371-3925","authenticated-orcid":false,"given":"Alix E.","family":"Rojas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4703-0007","authenticated-orcid":false,"given":"Luis Fernando Ni\u00f1o","family":"V\u00e1squez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,3]]},"reference":[{"key":"2431_CR1","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.mri.2019.05.028","volume":"61","author":"MK Abd-Ellah","year":"2019","unstructured":"Abd-Ellah MK, Awad AI, Khalaf AA, Hamed HF. A review on brain tumor diagnosis from mri images: practical implications, key achievements, and lessons learned. Magnetic Resonance Imaging. 2019;61:300\u201318.","journal-title":"Magnetic Resonance Imaging"},{"key":"2431_CR2","doi-asserted-by":"crossref","unstructured":"Amin J, Sharif M, Haldorai A, Yasmin M, Nayak RS. Brain tumor detection and classification using machine learning: a comprehensive survey. Complex Intell Syst. 2021;1\u201323.","DOI":"10.1007\/s40747-021-00563-y"},{"key":"2431_CR3","doi-asserted-by":"crossref","unstructured":"Babenko A, Slesarev A, Chigorin A, Lempitsky V. Neural codes for image retrieval. Lecture Notes in Computer Science 8689 LNCS, pp 584\u2013599 (2014)","DOI":"10.1007\/978-3-319-10590-1_38"},{"key":"2431_CR4","doi-asserted-by":"crossref","unstructured":"Bouchaour N, Mazouzi S. Deep pattern-based tumor segmentation in brain mris. Neural Comput Appl. 2022;1\u201310.","DOI":"10.1007\/s00521-022-07422-y"},{"key":"2431_CR5","unstructured":"Cheng J. Brain Tumor Dataset. Figshare (4 2017). 10.6084\/m9.figshare.1512427.v5, Retrieved 3 Oct 2020 from https:\/\/figshare.com\/articles\/dataset\/brain_tumor_dataset\/1512427"},{"issue":"10","key":"2431_CR6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0140381","volume":"10","author":"J Cheng","year":"2015","unstructured":"Cheng J, Huang W, Cao S, Yang R, Yang W, Yun Z, Wang Z, Feng Q. Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS One. 2015;10(10): e0140381.","journal-title":"PLoS One."},{"key":"2431_CR7","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition; 2016. pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2431_CR8","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.cogsys.2018.12.015","volume":"57","author":"R Jain","year":"2019","unstructured":"Jain R, Jain N, Aggarwal A, Hemanth DJ. Convolutional neural network based Alzheimer\u2019s disease classification from magnetic resonance brain images. Cogn Syst Res. 2019;57:147\u201359.","journal-title":"Cogn Syst Res"},{"key":"2431_CR9","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/B978-0-12-815739-8.00010-9","volume-title":"Machine learning","author":"WH Lopez Pinaya","year":"2020","unstructured":"Lopez Pinaya WH, Vieira S, Garcia-Dias R, Mechelli A. Chapter 10\u2014convolutional neural networks. In: Mechelli A, Vieira S, editors. Machine learning. New York: Academic Press; 2020. p. 173\u201391."},{"key":"2431_CR10","doi-asserted-by":"crossref","unstructured":"Saad NM, Bakar SARSA, Muda AS, Mokji MM. Review of brain lesion detection and classification using neuroimaging analysis techniques. Jurnal Teknologi. 2015;74(6).","DOI":"10.11113\/jt.v74.4670"},{"key":"2431_CR11","doi-asserted-by":"crossref","unstructured":"Sharma AK, Nandal A, Dhaka A, Zhou L, Alhudhaif A, Alenezi F, Polat K. Brain tumor classification using the modified resnet50 model based on transfer learning. Biomed Signal Process Control. 2023;86.","DOI":"10.1016\/j.bspc.2023.105299"},{"key":"2431_CR12","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"2431_CR13","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/RBME.2022.3185292","volume":"16","author":"TA Soomro","year":"2023","unstructured":"Soomro TA, Zheng L, Afifi AJ, Ali A, Soomro S, Yin M, Gao J. Image segmentation for mr brain tumor detection using machine learning: a review. IEEE Rev Biomed Eng. 2023;16:70\u201390.","journal-title":"IEEE Rev Biomed Eng"},{"key":"2431_CR14","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.compmedimag.2019.05.001","volume":"75","author":"ZNK Swati","year":"2019","unstructured":"Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, Lu J. Brain tumor classification for mr images using transfer learning and fine-tuning. Comput Med Imaging Graph. 2019;75:34\u201346.","journal-title":"Comput Med Imaging Graph"},{"key":"2431_CR15","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.cogsys.2018.12.007","volume":"54","author":"M Talo","year":"2019","unstructured":"Talo M, Baloglu UB, \u00d6zal Y\u0131ld\u0131r\u0131m M, Rajendra Acharya U. Application of deep transfer learning for automated brain abnormality classification using mr images. Cogn Syst Res. 2019;54:176\u201388.","journal-title":"Cogn Syst Res"},{"key":"2431_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.mehy.2020.109922","volume":"143","author":"Z Ullah","year":"2020","unstructured":"Ullah Z, Farooq MU, Lee SH, An D. A hybrid image enhancement based brain mri images classification technique. Med Hypotheses. 2020;143: 109922.","journal-title":"Med Hypotheses"},{"key":"2431_CR17","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging7040066","author":"JM Valverde","year":"2021","unstructured":"Valverde JM, Imani V, Abdollahzadeh A, De Feo R, Prakash M, Ciszek R, Tohka J. Transfer learning in magnetic resonance brain imaging: a systematic review. J Imaging. 2021. https:\/\/doi.org\/10.3390\/jimaging7040066.","journal-title":"J Imaging"},{"key":"2431_CR18","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.mri.2019.05.043","volume":"61","author":"A Wadhwa","year":"2019","unstructured":"Wadhwa A, Bhardwaj A, Singh Verma V. A review on brain tumor segmentation of mri images. Magn Reson Imaging. 2019;61:247\u201359.","journal-title":"Magn Reson Imaging"},{"key":"2431_CR19","doi-asserted-by":"publisher","first-page":"804","DOI":"10.3389\/fnins.2018.00804","volume":"12","author":"Y Yang","year":"2018","unstructured":"Yang Y, Yan LF, Zhang X, Han Y, Nan HY, Hu YC, Hu B, Yan SL, Zhang J, Cheng DL, et al. Glioma grading on conventional mr images: a deep learning study with transfer learning. Front Neurosci. 2018;12:804.","journal-title":"Front Neurosci"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-02431-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-023-02431-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-02431-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T19:06:14Z","timestamp":1704308774000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-023-02431-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,3]]},"references-count":19,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["2431"],"URL":"https:\/\/doi.org\/10.1007\/s42979-023-02431-7","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,3]]},"assertion":[{"value":"15 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No funds or grants were received for this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"142"}}