{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T07:12:26Z","timestamp":1763536346931,"version":"3.44.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-025-20633-4","type":"journal-article","created":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T02:58:48Z","timestamp":1738378728000},"page":"36673-36692","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Impact of preprocessing techniques on MRI-based brain tumor detection"],"prefix":"10.1007","volume":"84","author":[{"given":"Tanima","family":"Ghosh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N.","family":"Jayanthi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,1]]},"reference":[{"issue":"1","key":"20633_CR1","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1038\/s41598-024-52823-9","volume":"14","author":"MZ Khaliki","year":"2024","unstructured":"Khaliki MZ, Ba\u015farslan MS (2024) Brain tumor detection from images and comparison with transfer learning methods and 3-layer CNN. Sci Rep 14(1):2664","journal-title":"Sci Rep"},{"issue":"16","key":"20633_CR2","doi-asserted-by":"publisher","first-page":"4172","DOI":"10.3390\/cancers15164172","volume":"15","author":"AB Abdusalomov","year":"2023","unstructured":"Abdusalomov AB, Mukhiddinov M, Whangbo TK (2023) Brain tumor detection based on deep learning approaches and magnetic resonance imaging. Cancers 15(16):4172","journal-title":"Cancers"},{"issue":"5","key":"20633_CR3","doi-asserted-by":"publisher","first-page":"e232","DOI":"10.1016\/S2589-7500(19)30108-6","volume":"1","author":"L Faes","year":"2019","unstructured":"Faes L, Wagner SK, Fu DJ, Liu X, Korot E, Ledsam JR, Keane PA (2019) Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. The Lancet Digital Health 1(5):e232\u2013e242","journal-title":"The Lancet Digital Health"},{"issue":"1","key":"20633_CR4","first-page":"21","volume":"1","author":"H Selvaraj","year":"2019","unstructured":"Selvaraj H, Selvi ST, Selvathi D, Gewali LJR (2019) Brain MRI slices classification using least squares support vector machine. In:Int J Intell Comput Med Sci Image Process 1(1):21\u201333","journal-title":"In:Int J Intell Comput Med Sci Image Process"},{"issue":"2","key":"20633_CR5","first-page":"275","volume":"27","author":"MS Kabbur","year":"2018","unstructured":"Kabbur MS (2018) An efficient multiclass medical image cbir system based on classification and clustering. J Intell Syst 27(2):275\u2013290","journal-title":"J Intell Syst"},{"key":"20633_CR6","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.neucom.2017.05.025","volume":"266","author":"A Qayyum","year":"2017","unstructured":"Qayyum A, Anwar SM, Awais M, Majid M (2017) Medical image retrieval using deep convolutional neural network. Neurocomputing 266:8\u201320","journal-title":"Neurocomputing"},{"key":"20633_CR7","unstructured":"Al WA, Yun ID (2019) Reinforcing medical image classifier to improve generalization on small datasets. arXiv preprint arXiv:1909.05630"},{"key":"20633_CR8","doi-asserted-by":"crossref","unstructured":"Mohanarathinam A (2020) Enhanced image filtrationusing threshold based anisotropic filter for brain tumor image segmentation. In: 2020 3rd international conference on intelligent sustainable systems (ICISS). IEEE, pp 308\u2013316","DOI":"10.1109\/ICISS49785.2020.9315924"},{"key":"20633_CR9","first-page":"1726","volume":"45","author":"S Bama","year":"2021","unstructured":"Bama S, Velumani R, Prakash NB, Hemalakshmi GR, Mohanarathinam A (2021) Automatic segmentation of melanoma using superpixel region growing technique. Mater Today: Proc 45:1726\u20131732","journal-title":"Mater Today: Proc"},{"issue":"5","key":"20633_CR10","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1166\/jmihi.2021.3376","volume":"11","author":"M Yacin Sikkandar","year":"2021","unstructured":"Yacin Sikkandar M, Sudharsan NM, Balakirshnan S, Prakash NB, Hemalakshmi GR, Mohanarathinam A (2021) Accurate hotspot segmentation in thermal breast images with gaussian mixture model superpixels. J Med Imaging Health Informatics 11(5):1327\u20131333","journal-title":"J Med Imaging Health Informatics"},{"issue":"2","key":"20633_CR11","first-page":"73","volume":"2","author":"M Saii","year":"2017","unstructured":"Saii M, Kraitem Z (2017) Automatic brain tumor detection in MRI using image processing techniques. Biomed Stat Informatics 2(2):73\u201376","journal-title":"Biomed Stat Informatics"},{"key":"20633_CR12","doi-asserted-by":"crossref","unstructured":"Rashid MHO, Mamun MA, Hossain MA, Uddin MP (2018) Brain tumor detection using anisotropic filtering, SVM classifier and morphological operation from MR images. In: 2018 international conference on computer, communication, chemical, material and electronic engineering (IC4ME2).\u00a0IEEE, pp 1\u20134","DOI":"10.1109\/IC4ME2.2018.8465613"},{"issue":"1","key":"20633_CR13","first-page":"9749108","volume":"2017","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 2017(1):9749108","journal-title":"Int J Biomed Imaging"},{"issue":"1","key":"20633_CR14","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s40708-017-0075-5","volume":"5","author":"N Varuna Shree","year":"2018","unstructured":"Varuna Shree N, Kumar TNR (2018) Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain informatics 5(1):23\u201330","journal-title":"Brain informatics"},{"key":"20633_CR15","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 (2019) Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation. Front Comput Neurosci 13:56","journal-title":"Front Comput Neurosci"},{"key":"20633_CR16","doi-asserted-by":"publisher","first-page":"012012","DOI":"10.1088\/1757-899X\/336\/1\/012012","volume":"336","author":"HPA Tjahyaningtijas","year":"2018","unstructured":"Tjahyaningtijas HPA (2018) Brain tumor image segmentation in MRI image. IOP Conf Series: Mater Sci Eng 336:012012","journal-title":"IOP Conf Series: Mater Sci Eng"},{"issue":"3","key":"20633_CR17","doi-asserted-by":"publisher","first-page":"3887","DOI":"10.3182\/20140824-6-ZA-1003.02347","volume":"47","author":"CA Palma","year":"2014","unstructured":"Palma CA, Cappabianco FA, Ide JS, Miranda PA (2014) Anisotropic diffusion filtering operation and limitations-magnetic resonance imaging evaluation. IFAC Proc Volumes 47(3):3887\u20133892","journal-title":"IFAC Proc Volumes"},{"issue":"4","key":"20633_CR18","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.irbm.2021.06.003","volume":"43","author":"MO Khairandish","year":"2022","unstructured":"Khairandish MO, Sharma M, Jain V, Chatterjee JM, Jhanjhi NZ (2022) A hybrid CNN-SVM threshold segmentation approach for tumor detection and classification of MRI brain image. Irbm 43(4):290\u2013299","journal-title":"Irbm"},{"issue":"01","key":"20633_CR19","doi-asserted-by":"publisher","first-page":"14","DOI":"10.36548\/jiip.2020.1.002","volume":"2","author":"S Manoharan","year":"2020","unstructured":"Manoharan S (2020) Performance analysis of clustering based image segmentation techniques. J Innov Image Process 2(01):14\u201324","journal-title":"J Innov Image Process"},{"issue":"6","key":"20633_CR20","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1080\/21681163.2020.1776642","volume":"8","author":"A Jo\u00e3o","year":"2020","unstructured":"Jo\u00e3o A, Gambaruto A, Sequeira A (2020) Anisotropic gradient-based filtering for object segmentation in medical images. Computer Methods Biomech Biomed Eng Imaging Visualization 8(6):621\u2013630","journal-title":"Computer Methods Biomech Biomed Eng Imaging Visualization"},{"key":"20633_CR21","first-page":"5513500","volume":"1","author":"A Naseer","year":"2021","unstructured":"Naseer A, Yasir T, Azhar A (2021) Shakeel T and Zafar K 2021 Computer-aided brain tumor diagnosis: performance evaluation of deep learner CNN using augmented brain MRI. Int J Biomed Imaging 1:5513500","journal-title":"Int J Biomed Imaging"},{"key":"20633_CR22","doi-asserted-by":"crossref","unstructured":"Singh A, Chandra A, Kumar R, Singh K, Dey N (2019) Dark channel processing for medical image enhancement. In 2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE).\u00a0IEEE, pp 1\u20136","DOI":"10.1109\/WIECON-ECE48653.2019.9019993"},{"issue":"1","key":"20633_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-90428-8","volume":"11","author":"R Ranjbarzadeh","year":"2021","unstructured":"Ranjbarzadeh R, Bagherian Kasgari A, Jafarzadeh Ghoushchi S, Anari S, Naseri M, Bendechache M (2021) Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci Rep 11(1):1\u201317","journal-title":"Sci Rep"},{"issue":"12","key":"20633_CR24","doi-asserted-by":"publisher","first-page":"15","DOI":"10.9734\/jammr\/2020\/v32i1230539","volume":"32","author":"AM Sarhan","year":"2020","unstructured":"Sarhan AM (2020) Detection and Classification of Brain Tumor in MRI Images Using Wavelet Transform and Convolutional Neural Network. J Adv Med Med Res 32(12):15\u201326","journal-title":"J Adv Med Med Res"},{"issue":"4","key":"20633_CR25","first-page":"436","volume":"4","author":"NN Kachouie","year":"2008","unstructured":"Kachouie NN (2008) Anisotropic diffusion for medical image enhancement. Int J Image Process 4(4):436\u2013443","journal-title":"Int J Image Process"},{"key":"20633_CR26","first-page":"59","volume-title":"Anisotropic diffusion in image processing","author":"J Weickert","year":"1998","unstructured":"Weickert J (1998) Anisotropic diffusion in image processing. Teubner, Stuttgart, pp 59\u201360"},{"key":"20633_CR27","unstructured":"Singh TR, Roy S, Singh OI, Sinam T, Singh KM (2012) A new local adaptive thresholding technique in binarization. arXiv preprint arXiv:1201.5227"},{"key":"20633_CR28","first-page":"1","volume":"6","author":"P Nandihal","year":"2018","unstructured":"Nandihal P, Bhat VS, Pujari J (2018) Adaptive min max thresholds algorithm of microarray image denoising based on no subsampled contourlet transform. IJERT 6:1\u20135","journal-title":"IJERT"},{"issue":"04","key":"20633_CR29","doi-asserted-by":"publisher","first-page":"1850051","DOI":"10.1142\/S0217984918500513","volume":"32","author":"D Singh","year":"2018","unstructured":"Singh D, Kumar V (2018) Single image haze removal using integrated dark and bright channel prior. Mod Phys Lett B 32(04):1850051","journal-title":"Mod Phys Lett B"},{"key":"20633_CR30","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1080\/24699322.2017.1389395","volume":"22","author":"H Li","year":"2017","unstructured":"Li H, Chen C, Fang S, Zhao S (2017) Brain MR image segmentation using NAMS in pseudo-color. Computer Assisted Surg 22:170\u2013175","journal-title":"Computer Assisted Surg"},{"key":"20633_CR31","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.aeue.2017.03.002","volume":"75","author":"J Natarajan","year":"2017","unstructured":"Natarajan J, Sreedevi I (2017) Enhancement of ancient manuscript images by log based binarization technique. AEU-Int J Electronics Commun 75:15\u201322","journal-title":"AEU-Int J Electronics Commun"},{"key":"20633_CR32","doi-asserted-by":"publisher","first-page":"169474","DOI":"10.1016\/j.ijleo.2022.169474","volume":"265","author":"R Maurya","year":"2022","unstructured":"Maurya R, Wadhwani S (2022) An efficient method for brain image preprocessing with anisotropic diffusion filter and tumor segmentation. Optik 265:169474","journal-title":"Optik"},{"issue":"3","key":"20633_CR33","doi-asserted-by":"publisher","first-page":"100095","DOI":"10.1016\/j.neuri.2022.100095","volume":"2","author":"M Dweik","year":"2022","unstructured":"Dweik M, Ferretti R (2022) Integrating anisotropic filtering, level set methods and convolutional neural networks for fully automatic segmentation of brain tumors in magnetic resonance imaging. Neurosci Informatics 2(3):100095","journal-title":"Neurosci Informatics"},{"key":"20633_CR34","doi-asserted-by":"crossref","unstructured":"Hiremath S, Rani AS (2024) Image filtering using anisotropic diffusion for brain tumor detection. In: Applications of Parallel Data Processing for Biomedical Imaging.\u00a0IGI Global, pp 244\u2013260","DOI":"10.4018\/979-8-3693-2426-4.ch012"},{"issue":"3","key":"20633_CR35","doi-asserted-by":"publisher","first-page":"8","DOI":"10.4236\/jcc.2019.73002","volume":"7","author":"U Sara","year":"2019","unstructured":"Sara U, Akter M, Uddin MS (2019) Image quality assessment throug FSIM SSIM, MSE and PSNR-a comparative study. J Computer Commun 7(3):8\u201318","journal-title":"J Computer Commun"},{"key":"20633_CR36","doi-asserted-by":"crossref","unstructured":"Shahajad M, Gambhir D, Gandhi R (2021) Features extraction for classification of brain tumor MRI images using support vector machine. In: 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, pp 767\u2013772\u00a0","DOI":"10.1109\/Confluence51648.2021.9377111"},{"key":"20633_CR37","doi-asserted-by":"publisher","first-page":"107960","DOI":"10.1016\/j.compeleceng.2022.107960","volume":"101","author":"R Vankdothu","year":"2022","unstructured":"Vankdothu R, Hameed MA, Fatima H (2022) A brain tumor identification and classification using deep learning based on CNN-LSTM method. Comput Electr Eng 101:107960","journal-title":"Comput Electr Eng"},{"key":"20633_CR38","doi-asserted-by":"crossref","unstructured":"Srinivas C, KS NP, Zakariah M, Alothaibi YA, Shaukat K, Partibane B, Awal H (2022) Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images. J Healthc Eng 2022(1):3264367","DOI":"10.1155\/2022\/3264367"},{"issue":"1","key":"20633_CR39","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/bioengineering10010018","volume":"10","author":"H Zain Eldin","year":"2022","unstructured":"Zain Eldin H, Gamel SA, El-Kenawy ESM, Alharbi AH, Khafaga DS, Ibrahim A (2022) Brain tumor detection and classification using deep learning and sine-cosine fitness grey wolf optimization. Bioengineering 10(1):18","journal-title":"Bioengineering"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20633-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-025-20633-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20633-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T12:35:24Z","timestamp":1757680524000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-025-20633-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,1]]},"references-count":39,"journal-issue":{"issue":"30","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["20633"],"URL":"https:\/\/doi.org\/10.1007\/s11042-025-20633-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2025,2,1]]},"assertion":[{"value":"21 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2025","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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}