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2000
Volume 21, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Accurate brain tumor classification is essential in neuro-oncology, as it directly informs treatment strategies and influences patient outcomes. This review comprehensively explores machine learning (ML) and deep learning (DL) models that enhance the accuracy and efficiency of brain tumor classification using medical imaging data, particularly Magnetic Resonance Imaging (MRI). As a noninvasive imaging technique, MRI plays a central role in detecting, segmenting, and characterizing brain tumors by providing detailed anatomical views that help distinguish various tumor types, including gliomas, meningiomas, and metastatic brain lesions. The review presents a detailed analysis of diverse ML approaches, from classical algorithms such as Support Vector Machines (SVM) and Decision Trees to advanced DL models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and hybrid architectures that combine multiple techniques for improved performance. Through comparative analysis of recent studies across various datasets, the review evaluates these methods using metrics such as accuracy, sensitivity, specificity, and AUC-ROC, offering insights into their effectiveness and limitations. Significant challenges in the field are examined, including the scarcity of annotated datasets, computational complexity requirements, model interpretability issues, and barriers to clinical integration. The review proposes future directions to address these challenges, highlighting the potential of multi-modal imaging that combines MRI with other imaging modalities, explainable AI frameworks for enhanced model transparency, and privacy-preserving techniques for securing sensitive patient data. This comprehensive analysis demonstrates the transformative potential of ML and DL in advancing brain tumor diagnosis while emphasizing the necessity for continued research and innovation to overcome current limitations and ensure successful clinical implementation for improved patient care.

© 2025 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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References

  1. OstromQ.T. FahmidehM.A. CoteD.J. MuskensI.S. SchrawJ.M. ScheurerM.E. BondyM.L. Risk factors for childhood and adult primary brain tumors.Neuro-oncol.201921111357137510.1093/neuonc/noz12331301133
     [Google Scholar]
  2. AmyotF. ArciniegasD.B. BrazaitisM.P. CurleyK.C. Diaz-ArrastiaR. GandjbakhcheA. HerscovitchP. HindsS.R.II ManleyG.T. PacificoA. RazumovskyA. RileyJ. SalzerW. ShihR. SmirniotopoulosJ.G. StockerD. A review of the effectiveness of neuroimaging modalities for the detection of traumatic brain injury.J. Neurotrauma201532221693172110.1089/neu.2013.330626176603
     [Google Scholar]
  3. Villanueva-MeyerJ.E. MabrayM.C. ChaS. Current clinical brain tumor imaging.Neurosurgery201781339741510.1093/neuros/nyx10328486641
     [Google Scholar]
  4. LuoQ. LiY. LuoL. DiaoW. Comparisons of the accuracy of radiation diagnostic modalities in brain tumor.Medicine (Baltimore)20189731e1125610.1097/MD.000000000001125630075495
     [Google Scholar]
  5. KimJ.J. GeanA.D. Imaging for the diagnosis and management of traumatic brain injury.Neurotherapeutics201181395310.1007/s13311‑010‑0003‑321274684
     [Google Scholar]
  6. BiQ. GoodmanK.E. KaminskyJ. LesslerJ. What is machine learning? A primer for the epidemiologist.Am. J. Epidemiol.201918812kwz18910.1093/aje/kwz18931509183
     [Google Scholar]
  7. TandelG.S. BalestrieriA. JujarayT. KhannaN.N. SabaL. SuriJ.S. Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm.Comput. Biol. Med.202012210380410.1016/j.compbiomed.2020.10380432658726
     [Google Scholar]
  8. AltherB. GantenbeinA.R. From first symptoms to diagnosis: Initial clinical presentation of primary brain tumors.Clin. Translational. Neuroscience.2020422514183X2096836810.1177/2514183X2096836
     [Google Scholar]
  9. McFaline-FigueroaJ.R. LeeE.Q. Brain Tumors.Am. J. Med.2018131887488210.1016/j.amjmed.2017.12.03929371158
     [Google Scholar]
  10. BucknerJ.C. Brown P.D. O'NeilB.P. Meyer F.B.. Central nervous system tumors.Mayo Clin Proc. 220078210127110.4065/82.10.1271
     [Google Scholar]
  11. PöhlmannJ. WellerM. MarcellusiA. Grabe-HeyneK. Krott-CoiL. RabarS. PollockR.F. High costs, low quality of life, reduced survival, and room for improving treatment: an analysis of burden and unmet needs in glioma.Front. Oncol.202414136860610.3389/fonc.2024.136860638571509
     [Google Scholar]
  12. NazirM. ShakilS. KhurshidK. Role of deep learning in brain tumor detection and classification (2015 to 2020): A review.Comput. Med. Imaging Graph.20219110194010.1016/j.compmedimag.2021.10194034293621
     [Google Scholar]
  13. KhanM.S.I. RahmanA. DebnathT. KarimM.R. NasirM.K. BandS.S. MosaviA. DehzangiI. Accurate brain tumor detection using deep convolutional neural network.Comput. Struct. Biotechnol. J.2022204733474510.1016/j.csbj.2022.08.03936147663
     [Google Scholar]
  14. LouisD.N. PerryA. WesselingP. BratD.J. CreeI.A. Figarella-BrangerD. HawkinsC. NgH.K. PfisterS.M. ReifenbergerG. SoffiettiR. von DeimlingA. EllisonD.W. The 2021 WHO classification of tumors of the central nervous system: A summary.Neuro-oncol.20212381231125110.1093/neuonc/noab10634185076
     [Google Scholar]
  15. MesfinF.B. KarsonovichT. Al-DhahirM.A. Gliomas.StatPearlsTreasure Island (FL)StatPearls Publishing201728722904
     [Google Scholar]
  16. Barnholtz-SloanJ.S. OstromQ.T. CoteD. Epidemiology of brain tumors.Neurol. Clin.201836339541910.1016/j.ncl.2018.04.00130072062
     [Google Scholar]
  17. OgasawaraC. PhilbrickB.D. AdamsonD.C. Meningioma: A review of epidemiology, pathology, diagnosis, treatment, and future directions.Biomedicines20219331910.3390/biomedicines903031933801089
     [Google Scholar]
  18. MillerK.D. OstromQ.T. KruchkoC. PatilN. TihanT. CioffiG. FuchsH.E. WaiteK.A. JemalA. SiegelR.L. Barnholtz-SloanJ.S. Brain and other central nervous system tumor statistics, 2021.CA Cancer J. Clin.202171538140610.3322/caac.2169334427324
     [Google Scholar]
  19. SheikhM. De JesusO. Vestibular Schwannoma.In:StatPearlsTreasure Island (FL)StatPearls Publishing2024
     [Google Scholar]
  20. PopeW.B. Brain metastases: Neuroimaging.Handb. Clin. Neurol.20181498911210.1016/B978‑0‑12‑811161‑1.00007‑429307364
     [Google Scholar]
  21. AmmariS. Pitre-ChampagnatS. DercleL. ChouzenouxE. MoallaS. ReuzeS. TalbotH. MokoyokoT. HadchitiJ. DiffetocqS. VolkA. El HaikM. LakissS. BalleyguierC. LassauN. BidaultF. Influence of magnetic field strength on magnetic resonance imaging radiomics features in brain imaging, an in vitro and in vivo study.Front. Oncol.20211054166310.3389/fonc.2020.54166333552944
     [Google Scholar]
  22. KaurR. DoegarA. Localization and classification of brain tumor using machine learning & deep learning techniques.Int. J. Innov. Technol. Explor. Eng.20198922783075
     [Google Scholar]
  23. KaufmannT.J. SmitsM. BoxermanJ. HuangR. BarboriakD.P. WellerM. ChungC. TsienC. BrownP.D. ShankarL. GalanisE. GerstnerE. van den BentM.J. BurnsT.C. ParneyI.F. DunnG. BrastianosP.K. LinN.U. WenP.Y. EllingsonB.M. Consensus recommendations for a standardized brain tumor imaging protocol for clinical trials in brain metastases.Neuro-oncol.202022675777210.1093/neuonc/noaa03032048719
     [Google Scholar]
  24. MabrayM.C. BarajasR.F.Jr ChaS. Modern brain tumor imaging.Brain Tumor Res. Treat.20153182310.14791/btrt.2015.3.1.825977902
     [Google Scholar]
  25. RobertsT.A. HyareH. AgliardiG. HipwellB. d’EspositoA. IanusA. Breen-NorrisJ.O. RamasawmyR. TaylorV. AtkinsonD. PunwaniS. LythgoeM.F. SiowB. BrandnerS. ReesJ. PanagiotakiE. AlexanderD.C. Walker-SamuelS. Noninvasive diffusion magnetic resonance imaging of brain tumour cell size for the early detection of therapeutic response.Sci. Rep.2020101922310.1038/s41598‑020‑65956‑432514049
     [Google Scholar]
  26. LaiP.H. ChungH.W. ChangH.C. FuJ.H. WangP.C. HsuS.H. HsuS.S. LinH.S. ChuangT.C. Susceptibility-weighted imaging provides complementary value to diffusion-weighted imaging in the differentiation between pyogenic brain abscesses, necrotic glioblastomas, and necrotic metastatic brain tumors.Eur. J. Radiol.2019117566110.1016/j.ejrad.2019.05.02131307653
     [Google Scholar]
  27. DeyN. RajinikanthV. ShiF. TavaresJ.M.R.S. MoraruL. Arvind KarthikK. LinH. KamalanandK. EmmanuelC. Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality.Biocybern. Biomed. Eng.201939384385610.1016/j.bbe.2019.07.005
     [Google Scholar]
  28. KaifiR. A review of recent advances in brain tumor diagnosis based on ai-based classification.Diagnostics20231318300710.3390/diagnostics1318300737761373
     [Google Scholar]
  29. BarnesS.R.S. HaackeE.M. Susceptibility-weighted imaging: Clinical angiographic applications.Magn. Reson. Imaging Clin. N. Am.2009171476110.1016/j.mric.2008.12.00219364599
     [Google Scholar]
  30. MesséA. CaplainS. Pélégrini-IssacM. BlanchoS. LévyR. AghakhaniN. MontreuilM. BenaliH. LehéricyS. Specific and evolving resting-state network alterations in post-concussion syndrome following mild traumatic brain injury.PLoS One201386e6547010.1371/journal.pone.006547023755237
     [Google Scholar]
  31. HuR. HochM.J. Application of diffusion weighted imaging and diffusion tensor imaging in the pretreatment and post-treatment of brain tumor.Radiol. Clin. North Am.202159333534710.1016/j.rcl.2021.01.00333926681
     [Google Scholar]
  32. Di IevaA. LamT. Alcaide-LeonP. BharathaA. MontaneraW. CusimanoM.D. Magnetic resonance susceptibility weighted imaging in neurosurgery: Current applications and future perspectives.J. Neurosurg.201512361463147510.3171/2015.1.JNS14234926207600
     [Google Scholar]
  33. AshbyF.G. Statistical analysis of fMRI data.MIT press201910.7551/mitpress/11557.001.0001
     [Google Scholar]
  34. MaheshB. Machine learning algorithms: A review.Int. J. Sci. Res20209110.21275/ART20203995
     [Google Scholar]
  35. EricksonB.J. Korfiatis P. Akkus Z. Machine learning for medical imaging.Radiographics2017372505515
     [Google Scholar]
  36. MohanM.M. SulochanaC.H. LathaT. Medical image denoising using multistage directional median filter. in 2015 International Conference on Circuits, Power and Computing Technologie.2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015] Nagercoil, India, 19-20 March 2015, pp. 1-610.1109/ICCPCT.2015.7159261.
     [Google Scholar]
  37. HashemiM. Enlarging smaller images before inputting into convolutional neural network: zero-padding vs. interpolation.J. Big Data2019619810.1186/s40537‑019‑0263‑7
     [Google Scholar]
  38. BoroleV.Y. NimbhoreS.S. KawthekarD.S.S. Image processing techniques for brain tumor detection: A review.Int. J.Emerging Trend. Technol. Computer. Sci.2015452
     [Google Scholar]
  39. MiottoR. WangF. WangS. JiangX. DudleyJ.T. Deep learning for healthcare: Review, opportunities and challenges.Brief. Bioinform.20181961236124610.1093/bib/bbx04428481991
     [Google Scholar]
  40. ZiedanR.H. MeadM.A. EltawelG.S. Selecting the appropriate feature extraction tech- niques for automatic medical images classification.Int. J.201641
     [Google Scholar]
  41. AminJ. SharifM. YasminM. FernandesS.L. A distinctive approach in brain tumor detection and classification using MRI.Pattern Recognit. Lett.202013911812710.1016/j.patrec.2017.10.036
     [Google Scholar]
  42. IslamA. RezaS.M.S. IftekharuddinK.M. Multifractal texture estimation for detection and segmentation of brain tumors.IEEE Trans. Biomed. Eng.201360113204321510.1109/TBME.2013.227138323807424
     [Google Scholar]
  43. IslamA. RezaS.M.S. IftekharuddinK.M. Tumor detection and classification of MRI brain image using different wavelet transforms and support vector machines.42nd International Conference on Telecommunications and Signal Processing (TSP).Budapest, Hungary, 01-03 July 2019, pp. 505-50810.1109/TSP.2019.8769040
     [Google Scholar]
  44. XuX. ZhangX. TianQ. ZhangG. LiuY. CuiG. MengJ. WuY. LiuT. YangZ. LuH. Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI.Int. J. CARS201712464565610.1007/s11548‑017‑1522‑828110476
     [Google Scholar]
  45. KaplanK. KayaY. KuncanM. ErtunçH.M. Brain tumor classification using modified local binary patterns (LBP) feature extraction methods.Med. Hypotheses202013910969610.1016/j.mehy.2020.10969632234609
     [Google Scholar]
  46. AfzaF. KhanM.A. SharifM. RehmanA. Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection.Microsc. Res. Tech.20198291471148810.1002/jemt.2330131168871
     [Google Scholar]
  47. LakshmiA. ArivoliT. Pallikonda RajasekaranM. A novel M-ACA-based tumor segmen- tation and DAPP feature extraction with PPCSO-PKC-based MRI classification.Arab. J. Sci. Eng.201843127095711110.1007/s13369‑017‑2966‑4
     [Google Scholar]
  48. AdairJ. BrownleeA. OchoaG. Evolutionary algorithms with linkage information for feature selection in brain-computer interfaces.16th UK Workshop on Computational IntelligenceLancaster, UK, September 7–9, 2016
     [Google Scholar]
  49. ArakeriM.P. ReddyG.R.M. Computer-aided diagnosis system for tissue characterization of brain tumor on magnetic resonance images.Signal Image Video Process.20159240942510.1007/s11760‑013‑0456‑z
     [Google Scholar]
  50. WangS. ZhangY. DongZ. DuS. JiG. YanJ. YangJ. WangQ. FengC. PhillipsP. Feed‐forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection.Int. J. Imaging Syst. Technol.201525215316410.1002/ima.22132
     [Google Scholar]
  51. AbbasiS. TajeripourF. Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient.Neurocomputing201721952653510.1016/j.neucom.2016.09.051
     [Google Scholar]
  52. ZöllnerF.G. EmblemK.E. SchadL.R. SVM-based glioma grading: Optimization by feature reduction analysis.Z. Med. Phys.201222320521410.1016/j.zemedi.2012.03.00722503911
     [Google Scholar]
  53. HuangG.B. ZhuQ.Y. SiewC.K. Extreme learning machine: Theory and applications.Neurocomputing2006701-348950110.1016/j.neucom.2005.12.126
     [Google Scholar]
  54. BhateleK.R. BhadauriaS.S. Brain structural disorders detection and classification approaches: A review.Artif. Intell. Rev.20205353349340110.1007/s10462‑019‑09766‑9
     [Google Scholar]
  55. SchmidhuberJ. Deep learning in neural networks: An overview.Neural Netw.2015618511710.1016/j.neunet.2014.09.00325462637
     [Google Scholar]
  56. HuA. RazmjooyN. Brain tumor diagnosis based on metaheuristics and deep learning.Int. J. Imaging Syst. Technol.202131265766910.1002/ima.22495
     [Google Scholar]
  57. SahaaiM.B. Brain tumor detection using DNN algorithm. Turk. J. Comput. Math. Educ.. 202112113338334510.17762/turcomat.v12i11.6379
     [Google Scholar]
  58. GorachT. Deep convolutional neural networks-a review.Int. J. Res. Eng. Technol2018507439452
     [Google Scholar]
  59. OgundokunR.O. MaskeliunasR. MisraS. DamaševičiusR. Improved CNN based on batch normalization and adam optimizer.International Conference on Computational Science and Its Applications2022Springer.59360410.1007/978‑3‑031‑10548‑7_43
     [Google Scholar]
  60. Abdelaziz IsmaelS.A. MohammedA. HefnyH. An enhanced deep learning approach for brain cancer MRI images classification using residual networks.Artif. Intell. Med.202010210177910.1016/j.artmed.2019.10177931980109
     [Google Scholar]
  61. DeepakS. AmeerP.M. Brain tumor classification using deep CNN features via transfer learning.Comput. Biol. Med.201911110334510.1016/j.compbiomed.2019.10334531279167
     [Google Scholar]
  62. HuangZ. XuH. SuS. WangT. LuoY. ZhaoX. LiuY. SongG. ZhaoY. A computer-aided diagnosis system for brain magnetic resonance imaging images using a novel differential feature neural network.Comput. Biol. Med.202012110381810.1016/j.compbiomed.2020.10381832568685
     [Google Scholar]
  63. Díaz-PernasF.J. Martínez-ZarzuelaM. Antón-RodríguezM. A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network. in healthcare.Healthcare2021915310.3390/healthcare9020153
     [Google Scholar]
  64. GuanY. AamirM. Rahman Z. Ali A. A framework for efficient brain tumor classification using MRI images.Math Biosci Eng.2021185579010.3934/mbe.2021292
     [Google Scholar]
  65. Gab AllahA.M. SarhanA.M. ElshennawyN.M. Classification of brain MRI tumor images based on deep Learning PGGAN augmentation.Diagnostics20211112234310.3390/diagnostics1112234334943580
     [Google Scholar]
  66. GeC. GuI.Y.H. JakolaA.S. YangJ. Deep semi-supervised learning for brain tumor classification.BMC Med. Imaging20202018710.1186/s12880‑020‑00485‑032727476
     [Google Scholar]
  67. YangY. YanL.F. ZhangX. HanY. NanH.Y. HuY.C. HuB. YanS.L. ZhangJ. ChengD.L. GeX.W. CuiG.B. ZhaoD. WangW. Glioma grading on conventional MR images: A deep learning study with transfer learning.Front. Neurosci.20181280410.3389/fnins.2018.0080430498429
     [Google Scholar]
  68. AswathyA. Vinod ChandraS. Detection of brain tumor abnormality from MRI FLAIR images using machine learning techniques. J. Inst. Eng. India Ser. B20221031097110410.1007/s40031‑022‑00721‑x
     [Google Scholar]
  69. GuptaT. GandhiT.K. GuptaR.K. PanigrahiB.K. Classification of patients with tumor using MR FLAIR images.Pattern Recognit. Lett.202013911211710.1016/j.patrec.2017.10.037
     [Google Scholar]
  70. ShenbagarajanA. RamalingamV. BalasubramanianC. PalanivelS. Tumor diagnosis in MRI brain image using ACM segmentation and ANN-LM classification techniques.Indian J. Sci. Technol.20169111210.17485/ijst/2016/v9i1/78766
     [Google Scholar]
  71. SultanH.H. SalemN.M. Al-AtabanyW. Multi-classification of brain tumor images using deep neural network.IEEE Access20197692156922510.1109/ACCESS.2019.2919122
     [Google Scholar]
  72. UllahZ. FarooqM.U. LeeS.H. AnD. A hybrid image enhancement based brain MRI images classification technique.Med. Hypotheses202014310992210.1016/j.mehy.2020.10992232682214
     [Google Scholar]
  73. KangJ. UllahZ. GwakJ. MRI-based brain tumor classification using ensemble of deep features and machine learning classifiers.Sensors2021216222210.3390/s2106222233810176
     [Google Scholar]
  74. LoC.M. ChenY-C. WengR-C. HsiehK.L-C. Intelligent glioma grading based on deep transfer learning of MRI radiomic features.Appl. Sci.2019922492610.3390/app9224926
     [Google Scholar]
  75. KulkarniS.M. SundariG. Comparative analysis of performance of deep cnn based frame- work for brain mri classification using transfer learning.J. Eng. Sci. Technol.202116429012917
     [Google Scholar]
  76. DecuyperM. BonteS. DeblaereK. Van HolenR. Automated MRI based pipeline for segmentation and prediction of grade, IDH mutation and 1p19q co-deletion in glioma.Comput. Med. Imaging Graph.20218810183110.1016/j.compmedimag.2020.10183133482430
     [Google Scholar]
  77. JiaZ. ChenD. Brain tumor identification and classification of MRI images using deep learning techniques.IEEE Access2020
     [Google Scholar]
  78. TiwariP. SachdevaJ. AhujaC.K. KhandelwalN. Computer aided diagnosis system-a decision support system for clinical diagnosis of brain tumours.Int. J. Comput. Intell.201710110411910.2991/ijcis.2017.10.1.8
     [Google Scholar]
  79. MohsenH. El-DahshanE-S.A. El-HorbatyE-S.M. SalemA-B.M. Classification using deep learning neural networks for brain tumors.Future Comput. Inform. J.201831687110.1016/j.fcij.2017.12.001
     [Google Scholar]
  80. SenanE.M. JadhavM.E. RassemT.H. AljaloudA.S. MohammedB.A. Al-MekhlafiZ.G. Early diagnosis of brain tumour MRI images using hybrid techniques between deep and machine learning.Comput. Math. Methods Med.20222022111710.1155/2022/833083335633922
     [Google Scholar]
  81. HamdaouiH.E. BenfaresA. BoujrafS. ChaouiN.E.H. AlamiB. MaaroufiM. QjidaaH. High precision brain tumor classification model based on deep transfer learning and stacking concepts.Indonesian J. Elect. Eng. Computer. Sci.202124116717710.11591/ijeecs.v24.i1.pp167‑177
     [Google Scholar]
  82. AamirM. RahmanZ. DayoZ.A. AbroW.A. UddinM.I. KhanI. ImranA.S. AliZ. IshfaqM. GuanY. HuZ. A deep learning approach for brain tumor classification using MRI images.Comput. Electr. Eng.202210110810510.1016/j.compeleceng.2022.108105
     [Google Scholar]
  83. SwatiZ.N.K. ZhaoQ. KabirM. AliF. AliZ. AhmedS. LuJ. Brain tumor classification for MR images using transfer learning and fine-tuning.Comput. Med. Imaging Graph.201975344610.1016/j.compmedimag.2019.05.00131150950
     [Google Scholar]
  84. GilanieG. BajwaU.I. WaraichM.M. AnwarM.W. Risk-free WHO grading of astrocytoma using convolutional neural networks from MRI images.Multimedia Tools Appl.20218034295430610.1007/s11042‑020‑09970‑8
     [Google Scholar]
  85. ShrotS. SalhovM. DvorskiN. KonenE. AverbuchA. HoffmannC. Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.Neuroradiology201961775776510.1007/s00234‑019‑02195‑z30949746
     [Google Scholar]
  86. MasoodM. NazirT. NawazM. MehmoodA. RashidJ. KwonH.Y. MahmoodT. HussainA. A novel deep learning method for recognition and classification of brain tumors from MRI images.Diagnostics202111574410.3390/diagnostics1105074433919358
     [Google Scholar]
  87. GaurL. BhandariM. RazdanT. MallikS. ZhaoZ. Explanation-driven deep learning model for prediction of brain tumour status using MRI image data.Front. Genet.20221382266610.3389/fgene.2022.82266635360838
     [Google Scholar]
  88. ArtziM. RedmardE. TzemachO. ZeltserJ. GropperO. RothJ. ShoftyB. KozyrevD.A. ConstantiniS. Ben-SiraL. classification of pediatric posterior fossa tumors using convolutional neural network and tabular data.IEEE Access20219919669197310.1109/ACCESS.2021.3085771
     [Google Scholar]
  89. HaoR. Namdar K. Liu L. A transfer learning–based active learning framework for brain tumor classification.Front. Artificial. Intell.2021463576610.3389/frai.2021.635766
     [Google Scholar]
  90. KurcT. BakasS. RenX. BagariA. MomeniA. HuangY. ZhangL. KumarA. ThibaultM. QiQ. WangQ. KoriA. GevaertO. ZhangY. ShenD. KhenedM. DingX. KrishnamurthiG. Kalpathy-CramerJ. DavisJ. ZhaoT. GuptaR. SaltzJ. FarahaniK. segmentation and classification in digital pathology for glioma research: challenges and deep learning approaches.Front. Neurosci.2020142710.3389/fnins.2020.0002732153349
     [Google Scholar]
  91. PeiL. VidyaratneL. RahmanM.M. IftekharuddinK.M. Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images.Sci. Rep.20201011972610.1038/s41598‑020‑74419‑933184301
     [Google Scholar]
  92. WahlangI. SharmaP. SanyalS. SahaG. MajiA.K. Deep learning techniques for classification of brain MRI.Int. J. Intell. Syst. Technol. Appl.202019657158810.1504/IJISTA.2020.112441
     [Google Scholar]
  93. AsiriA.A ShafA. AamirM. Enhancing brain tumor diagnosis: An optimized CNN hyperparameter model for improved accuracy and reliability.Peer. J. Computer Science202410e187810.7717/peerj‑cs.1878
     [Google Scholar]
  94. AsiriA.A. ShafA. AliT. Advancing brain tumor detection: Harnessing the Swin Transformer’s power for accurate classification and performance analysis.Peer. J. Computer Science202410e186710.7717/peerj‑cs.1867
     [Google Scholar]
  95. AsiriA.A. ShafA.A. AliT. Transitioning from convolutional neural network to involutional neural network for resource efficient brain tumor detection.IEEE Access20231112308012309510.1109/ACCESS.2023.3326421
     [Google Scholar]
  96. AsiriA.A. ShafA. AliT. PashaM.A. AamirM. IrfanM. AlqahtaniS. AlghamdiA.J. AlghamdiA.H. AlshamraniA.F.A. AlelyaniM. AlamriS. Advancing brain tumor classification through fine-tuned vision transformers: A comparative study of pre-trained models.Sensors20232318791310.3390/s2318791337765970
     [Google Scholar]
  97. AsiriA.A. ShafA. AliT. AamirM. IrfanM. AlqahtaniS. MehdarK.M. HalawaniH.T. AlghamdiA.H. AlshamraniA.F.A. AlqhtaniS.M. Brain tumor detection and classification using fine-tuned CNN with ResNet50 and U-Net model: A study on TCGA-LGG and TCIA dataset for MRI applications.Life2023137144910.3390/life1307144937511824
     [Google Scholar]
  98. AsiriA.A. ShafA. AliT. ShakeelU. IrfanM. MehdarK.M. HalawaniH.T. AlghamdiA.H. AlshamraniA.F.A. AlqhtaniS.M. Exploring the power of deep Learning: fine-tuned vision transformer for accurate and efficient brain tumour detection in MRI scans.Diagnostics20231312209410.3390/diagnostics1312209437370989
     [Google Scholar]
  99. AsiriA.A. AamirM. ShafA. Block-wise neural network for brain tumor identification in magnetic resonance images.Comput. Mater. Continua20227335735575310.32604/cmc.2022.031747
     [Google Scholar]
  100. AsiriA Multi-level deep generative adversarial networks for brain tumor classification on magnetic resonance images.Intell. Automation. Soft Computing202336112710.32604/iasc.2023.032391
     [Google Scholar]
  101. KhanA.A. MahendranR.K. PerumalK. FaheemM. Dual-3DM 3 AD: mixed transformer based semantic segmentation and triplet pre-processing for early multi-class Alzheimer’s diagnosis.IEEE Trans. Neural Syst. Rehabil. Eng.20243269670710.1109/TNSRE.2024.335772338261494
     [Google Scholar]
  102. KhanA.A. MadendranR.K. ThirunavukkarasuU. FaheemM. D 2 PAM : Epileptic seizures prediction using adversarial deep dual patch attention mechanism.CAAI Trans. Intell. Technol.20238375576910.1049/cit2.12261
     [Google Scholar]
  103. AlhussenA. Anul HaqM. Ahmad KhanA. MahendranR.K. KadryS. XAI-RACapsNet: Relevance aware capsule network-based breast cancer detection using mammography images via explainability O-net ROI segmentation.Expert Syst. Appl.202526112546110.1016/j.eswa.2024.125461
     [Google Scholar]
  104. AlqarafiA. Ahmad KhanA. Kumar MahendranR. Al-SaremM. AlbalwyF. Multi-scale GC-T2: Automated region of interest assisted skin cancer detection using multi-scale graph convolution and tri-movement based attention mechanism.Biomed. Signal Process. Control20249510631310.1016/j.bspc.2024.106313
     [Google Scholar]
  105. KujurA. RazaZ. KhanA.A. WechtaisongC. Data complexity based evaluation of the model dependence of brain MRI images for classification of brain tumor and Alzheimer’s disease.IEEE Access20221011211711213310.1109/ACCESS.2022.3216393
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056365191250602124819
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Challenges and Advances in Classifying Brain Tumors: An Overview of Machine, Deep Learning, and Hybrid Approaches with Future Perspectives in Medical Imaging
Curr. Med. Imaging 21, E15734056365191 (2025); https://doi.org/10.2174/0115734056365191250602124819
/content/journals/cmir/10.2174/0115734056365191250602124819
/content/journals/cmir/10.2174/0115734056365191250602124819
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  • Article Type:     Review Article
Keyword(s): Advanced classification techniques; Artificial intelligence in healthcare; Diagnostic imaging; Integrated learning approaches; Magnetic resonance imaging; Neural network models; Tumor detection

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