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

Aims:

In the dynamic landscape of healthcare, integrating Artificial Intelligence paradigms has become essential for sophisticated brain image analysis, especially in tumor detection. This research addresses the need for heightened learning precision in handling sensitive medical images by introducing the Fragmented Segment Detection Technique.

Background:

The ever-evolving healthcare landscape demands advanced methods for brain image analysis, particularly in detecting tumors. This study responds to this need by introducing the Feature Segmentation and Detection Technique (FSDT), a novel approach designed to identify brain tumors precisely using MRI images. The focus is on enhancing detection accuracy, even for diminutive tumors.

The primary objective of this study is to introduce and evaluate the efficacy of FSDT in identifying and sizing brain tumors through advanced medical image analysis. The proposed technique utilizes cross-section segmentation and pixel distribution analysis to improve detection accuracy, particularly in size-based tumor detection scenarios.

Methods:

The proposed technique commences by fragmenting the input through cross-section segmentation, enabling meticulous separation of pixel distribution in various sections. A Convolutional Neural Network then independently operates sequentially on the minimum and maximum representations. The segmented cross-section feature, exhibiting maximum accuracy, is employed in the neural network training process. Fine-tuning of the neural network optimizes feature distribution and pixel arrangements, specifically in consecutive size-based tumor detection scenarios.

Results:

The FSDT employs cross-sectional segmentation and pixel distribution analysis to enhance detection accuracy by leveraging a diverse dataset encompassing central nervous system CNS tumors. Comparative evaluations against existing methods, including ERV-Net, MRCNN, and ENet-B0, reveal FSDT's superiority in accuracy, training rate, analysis ratio, precision, recall, F1-score, and computational efficiency. The proposed technique demonstrates a remarkable 10.45% increase in accuracy, 14.12% in training rate, and a 10.78% reduction in analysis time.

Conclusion:

The proposed FSDT emerges as a promising solution for advancing the accurate identification and sizing of brain tumors through cutting-edge medical image analysis. The demonstrated improvements in accuracy, training rate, and analysis time showcase its potential for effective real-world healthcare applications.

2025 © 2025 The Author(s). Published by Bentham Science Publisher. The Author(s)
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2025-03-06
2025-06-18

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References

  1. DikiciE. RyuJ.L. DemirerM. BigelowM. WhiteR.D. SloneW. ErdalB.S. PrevedelloL.M. Automated brain metastases detection framework for t1-weighted contrast-enhanced 3D MRI.IEEE J. Biomed. Health Inform.202024102883289310.1109/JBHI.2020.298210332203040
     [Google Scholar]
  2. BalahaH.M. HassanA.E.S. A variate brain tumor segmentation, optimisation, and recognition framework.Artif. Intell. Rev.2022154
     [Google Scholar]
  3. FangL. WangX. Brain tumor segmentation based on the dual-path network of multi-modal MRI images.Pattern Recognit.202212410843410.1016/j.patcog.2021.108434
     [Google Scholar]
  4. DweikM. FerrettiR. Integrating anisotropic filtering, level set methods and convolutional neural networks for fully automatic segmentation of brain tumors in magnetic resonance imaging.Neuroscience Informatics20222310009510.1016/j.neuri.2022.100095
     [Google Scholar]
  5. LuS.L. LiaoH.C. HsuF.M. LiaoC.C. LaiF. XiaoF. The intracranial tumor segmentation challenge: Contour tumors on brain MRI for radiosurgery.Neuroimage202124411858510.1016/j.neuroimage.2021.11858534560272
     [Google Scholar]
  6. SoomroT.A. ZhengL. AfifiA.J. AliA. SoomroS. YinM. GaoJ. Image segmentation for mr brain tumor detection using machine learning: A review.IEEE Rev. Biomed. Eng.202235737636
     [Google Scholar]
  7. LeiX. YuX. ChiJ. WangY. ZhangJ. WuC. Brain tumor segmentation in MR images using a sparse constrained level set algorithm.Expert Syst. Appl.202116811426210.1016/j.eswa.2020.114262
     [Google Scholar]
  8. HuangZ. ZouS. WangG. ChenZ. ShenH. WangH. ZhangN. ZhangL. YangF. WangH. LiangD. NiuT. ZhuX. HuZ. ISA-Net: Improved spatial attention network for PET-CT tumor segmentation.Comput. Methods Programs Biomed.202222610712910.1016/j.cmpb.2022.10712936156438
     [Google Scholar]
  9. HuJ. GuX. GuX. Mutual ensemble learning for brain tumor segmentation.Neurocomputing2022504688110.1016/j.neucom.2022.06.058
     [Google Scholar]
  10. HabibH. AminR. AhmedB. HannanA. Hybrid algorithms for brain tumor segmentation, classification and feature extraction.J. Ambient Intell. Humaniz. Comput.20221352763278410.1007/s12652‑021‑03544‑8
     [Google Scholar]
  11. JiangM. ZhaiF. KongJ. A novel deep learning model DDU-net using edge features to enhance brain tumor segmentation on MR images.Artif. Intell. Med.202112110218010.1016/j.artmed.2021.10218034763802
     [Google Scholar]
  12. MaQ. ZhouS. LiC. LiuF. LiuY. HouM. ZhangY. DGRUnit: Dual graph reasoning unit for brain tumor segmentation.Comput. Biol. Med.202214910607910.1016/j.compbiomed.2022.10607936108413
     [Google Scholar]
  13. ZhangY. LuY. ChenW. ChangY. GuH. YuB. MSMANet: A multi-scale mesh aggregation network for brain tumor segmentation.Appl. Soft Comput.202111010773310.1016/j.asoc.2021.107733
     [Google Scholar]
  14. SaleemH. ShahidA.R. RazaB. Visual interpretability in 3D brain tumor segmentation network.Comput. Biol. Med.202113310441010.1016/j.compbiomed.2021.10441033894501
     [Google Scholar]
  15. LiuZ. TongL. ChenL. JiangZ. ZhouF. ZhangQ. ZhouH. Deep learning based brain tumor segmentation: A survey.Complex & Intelligent Systems2022941001102610.1007/s40747‑022‑00815‑5
     [Google Scholar]
  16. AlqazzazS. SunX. NokesL.D.M. YangH. YangY. XuR. ZhangY. YangX. Combined features in region of interest for brain tumor segmentation.J. Digit. Imaging202235493894610.1007/s10278‑022‑00602‑135293605
     [Google Scholar]
  17. ShahH.A. SaeedF. YunS. ParkJ.H. PaulA. KangJ.M. A robust approach for brain tumor detection in magnetic resonance images using finetuned efficientNet.IEEE Access202210654266543810.1109/ACCESS.2022.3184113
     [Google Scholar]
  18. YuW. KangH. SunG. LiangS. LiJ. Bio-inspired feature selection in brain disease detection via an improved sparrow search algorithm.IEEE Trans. Instrum. Meas.202272115
     [Google Scholar]
  19. AhmadS. ChoudhuryP.K. On the performance of deep transfer learning networks for brain tumor detection using MR images.IEEE Access202210590995911410.1109/ACCESS.2022.3179376
     [Google Scholar]
  20. OttomM. A. RahmanH. A. DinovI. D. Deep learning approach for 2D MRI brain tumor segmentation.IEEE J Transl Eng Health Med.2022101810.1109/JTEHM.2022.3176737
     [Google Scholar]
  21. LiangJ. YangC. ZengL. 3D PSwinBTS: An efficient transformer-based Unet using 3D parallel shifted windows for brain tumor segmentation.Digit. Signal Process.202213110378410.1016/j.dsp.2022.103784
     [Google Scholar]
  22. SunsuhiG.S. Albin JoseS. An adaptive eroded deep convolutional neural network for brain image segmentation and classification using inception resnetV2.Biomed. Signal Process. Control20227810386310.1016/j.bspc.2022.103863
     [Google Scholar]
  23. UllahZ. UsmanM. JeonM. GwakJ. Cascade multiscale residual attention CNNs with adaptive ROI for automatic brain tumor segmentation.Inf. Sci.20226081541155610.1016/j.ins.2022.07.044
     [Google Scholar]
  24. ZhouX. LiX. HuK. ZhangY. ChenZ. GaoX. ERV-Net: An efficient 3D residual neural network for brain tumor segmentation.Expert Syst. Appl.202117011456610.1016/j.eswa.2021.114566
     [Google Scholar]
  25. PeiL. BakasS. VossoughA. RezaS.M.S. DavatzikosC. IftekharuddinK.M. Longitudinal brain tumor segmentation prediction in MRI using feature and label fusion.Biomed. Signal Process. Control20205510164810.1016/j.bspc.2019.10164834354762
     [Google Scholar]
  26. SunJ. PengY. GuoY. LiD. Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN.Neurocomputing2021423344510.1016/j.neucom.2020.10.031
     [Google Scholar]
  27. MasoodM. NazirT. NawazM. JavedA. IqbalM. MehmoodA. Brain tumor localization and segmentation using mask RCNN.Front. Comput. Sci.202115615633810.1007/s11704‑020‑0105‑y
     [Google Scholar]
  28. XiongS. WuG. FanX. FengX. HuangZ. CaoW. ZhouX. DingS. YuJ. WangL. ShiZ. MRI-based brain tumor segmentation using FPGA-accelerated neural network.BMC Bioinformatics202122142110.1186/s12859‑021‑04347‑634493208
     [Google Scholar]
  29. IslamM.M. KashemM.A. Parametric active contour model-based tumor area segmentation from brain MRI images using minimum initial points.Iran Journal of Computer Science20214212513210.1007/s42044‑020‑00078‑8
     [Google Scholar]
  30. IqbalM.J. BajwaU.I. GilanieG. IftikharM.A. AnwarM.W. Automatic brain tumor segmentation from magnetic resonance images using superpixel-based approach.Multimedia Tools Appl.20228127384093842710.1007/s11042‑022‑13166‑7
     [Google Scholar]
  31. HossainA. IslamM.T. RahmanT. ChowdhuryM.E.H. TahirA. KiranyazS. MatK. BengG.K. SolimanM.S. Brain tumor segmentation and classification from sensor-based portable microwave brain imaging system using lightweight deep learning models.Biosensors202313330210.3390/bios1303030236979514
     [Google Scholar]
  32. BabuK.D. SinghC.S. Brain tumor segmentation through level based learning model.Comput. Syst. Sci. Eng.2023441709720
     [Google Scholar]
  33. RamyaD. LakshmiC. Design of novel brain tumor segmentation system using hybrid heuristic-aided multiscale self-guided attention mechanism-based adaptive unet+++ with 3D brain MRI images.Int. J. Pattern Recognit. Artif. Intell.2024385245100110.1142/S0218001424510017
     [Google Scholar]
  34. SunJ. HuM. WuX. TangC. LahzaH. WangS. ZhangY. MVSI-Net: Multi-view attention and multi-scale feature interaction for brain tumor segmentation.Biomed. Signal Process. Control20249510648410.1016/j.bspc.2024.106484
     [Google Scholar]
  35. https://www.kaggle.com/sartajbhuvaji/brain-tumor-classification-mri
/content/journals/cmir/10.2174/0115734056305784240821045425
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A Novel Fragmentation-based Approach for Accurate Segmentation of Small-sized Brain Tumors in MRI Images
Curr. Med. Imaging 21, E15734056305784 (2025); https://doi.org/10.2174/0115734056305784240821045425
/content/journals/cmir/10.2174/0115734056305784240821045425
/content/journals/cmir/10.2174/0115734056305784240821045425
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  • Article Type:     Review Article
Keyword(s): Artificial intelligence; Convolutional neural network; Feature extraction; Machine learning; Segmentation; Tumor detection

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