{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T17:05:37Z","timestamp":1770138337384,"version":"3.49.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"TUB\u0130TAK","award":["124E170"],"award-info":[{"award-number":["124E170"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s11517-025-03447-2","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T02:20:25Z","timestamp":1758594025000},"page":"197-217","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing AI-based decision support system with automatic brain tumor segmentation for EGFR mutation classification"],"prefix":"10.1007","volume":"64","author":[{"given":"Neslihan","family":"G\u00f6kmen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4354-7383","authenticated-orcid":false,"given":"Ozan","family":"Kocada\u011fl\u0131","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Serdar","family":"Cevik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cagdas","family":"Aktan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reza","family":"Eghbali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunlei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"issue":"1","key":"3447_CR1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s00005-012-0203-0","volume":"61","author":"S Agnihotri","year":"2020","unstructured":"Agnihotri S, Burrell KE, Wolf A, Jalali S, Hawkins C, Rutka JT, Zadeh G (2020) Glioblastoma, a brief review of history, molecular genetics, animal models and novel therapeutic strategies. Arch Immunol Ther Exp 61(1):25\u201341","journal-title":"Arch Immunol Ther Exp"},{"key":"3447_CR2","unstructured":"Shinojima N, Tada K, Shiraishi S, Kamiryo T, Kochi M, Nakamura H, ... & Ushio Y (2003) Prognostic value of epidermal growth factor receptor in patients with glioblastoma multiforme. Cancer Research, 63(20), 6962\u20136970"},{"issue":"1","key":"3447_CR3","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1593\/neo.04535","volume":"7","author":"DB Hoelzinger","year":"2005","unstructured":"Hoelzinger DB, Mariani L, Weis J, Woyke T, Berens TJ, McDonough WS, Sloan A, Coons SW, Berens ME (2005) Gene expression profile of glioblastoma multiforme invasive phenotype points to new therapeutic targets. Neoplasia (New York, NY) 7(1):7\u201316","journal-title":"Neoplasia (New York, NY)"},{"issue":"1","key":"3447_CR4","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1186\/s40478-022-01422-8","volume":"10","author":"J Li","year":"2022","unstructured":"Li J, Ek F, Olsson R, Belting M, Bengzon J (2022) Glioblastoma CD105+ cells define a SOX2\u2212 cancer stem cell-like subpopulation in the pre-invasive niche. Acta Neuropathol Commun 10(1):126","journal-title":"Acta Neuropathol Commun"},{"key":"3447_CR5","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/9749108","author":"NB Bahadure","year":"2017","unstructured":"Bahadure NB, Ray AK, Thethi HP (2017) Image analysis for MRI-based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int J Biomed Imaging. https:\/\/doi.org\/10.1155\/2017\/9749108","journal-title":"Int J Biomed Imaging"},{"issue":"3","key":"3447_CR6","doi-asserted-by":"publisher","first-page":"3701","DOI":"10.1007\/s11042-016-3401-7","volume":"77","author":"S Wang","year":"2018","unstructured":"Wang S, Du S, Atangana A, Liu Z, Lu Z (2018) Application of stationary wavelet entropy in pathological brain detection. Multimedia Tools Appl 77(3):3701\u20133714","journal-title":"Multimedia Tools Appl"},{"issue":"2","key":"3447_CR7","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1002\/ima.22132","volume":"25","author":"S Wang","year":"2015","unstructured":"Wang S, Zhang Y, Dong Z, Du S, Ji G, Yan J, Phillips P (2015) Feed-forward neural network optimized by hybridization of PSO & ABC for abnormal brain detection. Int J Imaging Syst Technol 25(2):153\u2013164","journal-title":"Int J Imaging Syst Technol"},{"key":"3447_CR8","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neuroimage.2014.12.061","volume":"108","author":"W Zhang","year":"2015","unstructured":"Zhang W, Li R, Deng H, Wang L, Lin W, Ji S, Shen D (2015) Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. Neuroimage 108:214\u2013224","journal-title":"Neuroimage"},{"key":"3447_CR9","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.eswa.2017.07.020","volume":"88","author":"O Kocadagli","year":"2017","unstructured":"Kocadagli O, Langari R (2017) Classification of EEG signals for epileptic seizures using hybrid artificial neural networks based wavelet transforms and fuzzy relations. Expert Syst Appl 88:419\u2013434","journal-title":"Expert Syst Appl"},{"key":"3447_CR10","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, & Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015 (pp. 234\u2013241). Springer International Publishing","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3447_CR11","first-page":"506","volume-title":"Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks","author":"H Dong","year":"2017","unstructured":"Dong H, Yang G, Liu F, Mo Y, Guo Y (2017) Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. Springer International Publishing, In Medical Image Understanding & Analysis, pp 506\u2013517"},{"issue":"1","key":"3447_CR12","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen D, Wu G, Suk H-I (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19(1):221\u2013248","journal-title":"Annu Rev Biomed Eng"},{"issue":"8","key":"3447_CR13","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1109\/TMI.2018.2805821","volume":"37","author":"C Ma","year":"2018","unstructured":"Ma C, Luo G, Wang K (2018) Concatenated & connected random forests with multiscale patch driven active contour model for automated brain tumor segmentation of MR images. IEEE Trans Med Imaging 37(8):1943\u20131954","journal-title":"IEEE Trans Med Imaging"},{"key":"3447_CR14","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.media.2017.10.002","volume":"43","author":"X Zhao","year":"2018","unstructured":"Zhao X, Wu Y, Song G, Li Z, Zhang Y, Fan Y (2018) A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal 43:98\u2013111","journal-title":"Med Image Anal"},{"issue":"1","key":"3447_CR15","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1186\/s12911-021-01687-4","volume":"21","author":"M Hajiabadi","year":"2021","unstructured":"Hajiabadi M, Alizadeh Savareh B, Emami H, Bashiri A (2021) Comparison of wavelet transformations to enhance convolutional neural network performance in brain tumor segmentation. BMC Med Inform Decis Mak 21(1):327","journal-title":"BMC Med Inform Decis Mak"},{"key":"3447_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102365","volume":"131","author":"I Mecheter","year":"2022","unstructured":"Mecheter I, Abbod M, Amira A, Zaidi H (2022) Deep learning with multiresolution handcrafted features for brain MRI segmentation. Artif Intell Med 131:102365","journal-title":"Artif Intell Med"},{"key":"3447_CR17","doi-asserted-by":"publisher","first-page":"10386","DOI":"10.1016\/j.bspc.2022.103863","volume":"78","author":"GS Sunsuhi","year":"2022","unstructured":"Sunsuhi GS, Jose SA (2022) An adaptive eroded deep convolutional neural network for brain image segmentation and classification using Inception ResnetV2. Biomed Signal Process Control 78:10386","journal-title":"Biomed Signal Process Control"},{"key":"3447_CR18","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, & Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In\u00a0Proceedings of the AAAI conference on artificial intelligence 31(1)","DOI":"10.1609\/aaai.v31i1.11231"},{"issue":"1","key":"3447_CR19","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s11060-022-03946-4","volume":"157","author":"O Haim","year":"2022","unstructured":"Haim O, Abramov S, Shofty B, Fanizzi C, DiMeco F, Avisdris N, Ram Z, Artzi M, Grossman R (2022) Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases. J Neuro-Oncol 157(1):63\u201369","journal-title":"J Neuro-Oncol"},{"issue":"12","key":"3447_CR20","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac7192","volume":"67","author":"R Cao","year":"2022","unstructured":"Cao R, Pang Z, Wang X, Du Z, Chen H, Liu J, Jiang X (2022) Radiomics evaluates the EGFR mutation status from the brain metastasis: a multi-center study. Phys Med Biol 67(12):125003","journal-title":"Phys Med Biol"},{"issue":"1","key":"3447_CR21","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1186\/s12967-023-04681-8","volume":"21","author":"Z Zhou","year":"2023","unstructured":"Zhou Z, Wang M, Zhao R, Shao Y, Xing L, Qiu Q, Yin Y (2023) A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis. J Transl Med 21(1):788","journal-title":"J Transl Med"},{"key":"3447_CR22","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, ... & Houlsby N (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"issue":"8","key":"3447_CR23","doi-asserted-by":"publisher","first-page":"205","DOI":"10.3390\/jimaging8080205","volume":"8","author":"AA Akinyelu","year":"2022","unstructured":"Akinyelu AA, Zaccagna F, Grist JT, Castelli M, Rundo L (2022) Brain tumor diagnosis using machine learning, convolutional neural networks, capsule neural networks and vision transformers, applied to MRI: a survey. J Imaging 8(8):205","journal-title":"J Imaging"},{"issue":"12","key":"3447_CR24","doi-asserted-by":"publisher","DOI":"10.3390\/app12125990","volume":"12","author":"Y Gulzar","year":"2022","unstructured":"Gulzar Y, Khan SA (2022) Skin lesion segmentation based on vision transformers and convolutional neural networks\u2014a comparative study. Appl Sci 12(12):5990","journal-title":"Appl Sci"},{"issue":"10","key":"3447_CR25","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber M-A, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp C, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SMS, Ryan M, Sarikaya D, Schwartz L, Shin H-C, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Van Leemput K (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993\u20132024","journal-title":"IEEE Trans Med Imaging"},{"issue":"5","key":"3447_CR26","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1016\/j.sigpro.2005.06.015","volume":"86","author":"I De","year":"2006","unstructured":"De I, Cha B (2006) A simple & efficient algorithm for multifocus image fusion using morphological wavelets. Signal Process 86(5):924\u2013936","journal-title":"Signal Process"},{"key":"3447_CR27","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1006\/gmip.1995.1022","volume":"57","author":"H Li","year":"1995","unstructured":"Li H, Manjunath BS, Mitra SK (1995) Multisensor image fusion using the wavelet transform. Graphical Models and Image Processing 57:235\u2013245","journal-title":"Graphical Models and Image Processing"},{"issue":"2","key":"3447_CR28","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1002\/cpa.10116","volume":"57","author":"E Candes","year":"2004","unstructured":"Candes E, Donoho DL (2004) New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities. Commun Pure Appl Math 57(2):219\u2013266","journal-title":"Commun Pure Appl Math"},{"issue":"3","key":"3447_CR29","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1137\/05064182X","volume":"5","author":"E Candes","year":"2006","unstructured":"Candes E, Demanet L, Donoho D, Ying L (2006) Fast discrete curvelet transforms. Multiscale Model Simul 5(3):861\u2013899","journal-title":"Multiscale Model Simul"},{"issue":"7","key":"3447_CR30","doi-asserted-by":"publisher","first-page":"1334","DOI":"10.1016\/j.sigpro.2009.01.012","volume":"89","author":"Q Zhang","year":"2009","unstructured":"Zhang Q, Guo B (2009) Multifocus image fusion using the nonsubsampled contourlet transform. Signal Process 89(7):1334\u20131346","journal-title":"Signal Process"},{"issue":"1","key":"3447_CR31","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.acha.2007.09.003","volume":"25","author":"G Easley","year":"2008","unstructured":"Easley G, Labate D, Lim WQ (2008) Sparse directional image representations using the discrete shearlet transform. Appl Comput Harmon Anal 25(1):25\u201343","journal-title":"Appl Comput Harmon Anal"},{"issue":"1","key":"3447_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2740960","volume":"42","author":"G Kutyniok","year":"2016","unstructured":"Kutyniok G, Lim WQ, Reisenhofer R (2016) Shearlab 3d: faithful digital shearlet transforms based on compactly supported shearlets. ACM Transact Math Soft (TOMS) 42(1):1\u201342","journal-title":"ACM Transact Math Soft (TOMS)"},{"issue":"6","key":"3447_CR33","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1049\/iet-ipr.2012.0558","volume":"7","author":"G Guorong","year":"2013","unstructured":"Guorong G, Luping X, Dongzhu F (2013) Multi-focus image fusion based on non-subsampled shearlet transform. IET Image Process 7(6):633\u2013639","journal-title":"IET Image Process"},{"key":"3447_CR34","doi-asserted-by":"crossref","unstructured":"Bhatia Y, Bajpayee A, Raghuvanshi D, & Mittal H (2019) Image captioning using Google's inception-resnet-v2 and recurrent neural network. In 2019 Twelfth International Conference on Contemporary Computing (IC3) (1\u20136). IEEE","DOI":"10.1109\/IC3.2019.8844921"},{"issue":"4","key":"3447_CR35","doi-asserted-by":"publisher","first-page":"375","DOI":"10.11152\/mu-4306","volume":"25","author":"I Mese","year":"2023","unstructured":"Mese I, Inan NG, Kocadagli O, Salmaslioglu A, Yildirim D (2023) ChatGPT-assisted deep learning model for thyroid nodule analysis: beyond artificial intelligence. Med Ultrasonography 25(4):375\u2013383","journal-title":"Med Ultrasonography"},{"key":"3447_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107921","volume":"243","author":"N GokmenInan","year":"2024","unstructured":"GokmenInan N, Kocadagli O, Yildirim D, Mese I, Kovan O (2024) Multi-class classification of thyroid nodules from automatic segmented ultrasound images: hybrid ResNet based UNet convolutional neural network approach. Comput Methods Programs Biomed 243:107921","journal-title":"Comput Methods Programs Biomed"},{"issue":"4","key":"3447_CR37","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"3447_CR38","doi-asserted-by":"publisher","first-page":"1350","DOI":"10.1109\/ACCESS.2017.2778745","volume":"6","author":"D Zhang","year":"2018","unstructured":"Zhang D, Zou L, Chen Y, He F (2018) Efficient and accurate hausdorff distance computation based on diffusion search. IEEE Access 6:1350\u20131361","journal-title":"IEEE Access"},{"key":"3447_CR39","doi-asserted-by":"publisher","first-page":"64758","DOI":"10.1109\/ACCESS.2023.3288017","volume":"11","author":"R Rajendran","year":"2023","unstructured":"Rajendran R, Rajagopal SK, Thanarajan T, Shankar K, Kumar S, Alsubaie NM (2023) Automated segmentation of brain tumor MRI images using deep learning. IEEE Access 11:64758\u201364768. https:\/\/doi.org\/10.1109\/ACCESS.2023.3288017","journal-title":"IEEE Access"},{"key":"3447_CR40","doi-asserted-by":"publisher","DOI":"10.32604\/csse.2023.032488","author":"S Rajagopal","year":"2023","unstructured":"Rajagopal S, Thanarajan T, Alotaibi Y, Alghamdi S (2023) Brain tumor: hybrid feature extraction based on UNet and 3DCNN. Comput Syst Sci Eng. https:\/\/doi.org\/10.32604\/csse.2023.032488","journal-title":"Comput Syst Sci Eng"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03447-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-025-03447-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03447-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T05:57:09Z","timestamp":1770098229000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-025-03447-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,23]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["3447"],"URL":"https:\/\/doi.org\/10.1007\/s11517-025-03447-2","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,23]]},"assertion":[{"value":"16 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 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 that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}]}}