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Abstract

Introduction:

Brain tumors are predicted from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan images. In recent years, image processing-based automated tools are developed to predict tumor areas with less human interference. However, such automated tools are suffering from computational complexity and reduced accuracy in certain critical images. In the proposed work, an Ideal Shallow Neural Network (ISNN) is utilized to improve the prediction accuracy, and the computational complexity is reduced by implementing an Artificial Jellyfish Optimization (AJO) algorithm for minimizing the feature dimensionality.

Methods:

The proposed method utilizes MRI images for the verification process as they are more informative than the CT scan image. The BRATS and the Kaggle datasets are used in this work and a Gabor filtering technique is used for noise reduction and a histogram equalization is used for enhancing the tumor boundary regions. The classification results observed from the AJO-ISNN are further forwarded towards the segmentation process and which uses the Centroid Weighted Segmentation (WCS) along with a Grasshopper Optimization Algorithm (GOA) for improving the segmentation over the boundary regions of the brain tumor.

Results:

The experimental result indicates a classification accuracy of 95.14% on the proposed AJO-ISNN model and AJO-ISNN is comparatively better than the Convolutional Neural Network (CNN) model accuracy of 85.41% and VGG 19 model accuracy of 93.75% while implemented with the AJO optimization model. Similarly, the Dice Similarity Coefficient of the proposed CWS-GOA also reaches 93.15% when performed with both BRATS and Kaggle datasets.

Conclusion:

Apart from the accuracy attainments the proposed work classifies and segments the tumor region in around 65 seconds on average of 200 image verifications and that is comparatively better than the previous multi-cascaded CNN and the InceptionV3 models.

© 2024 The Author(s). Published by Bentham Science Publisher.
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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2023-09-08
2025-01-10

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References

  1. ThayumanavanM. RamasamyA. An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier.Concurr. Eng. Res. Appl.202129326627410.1177/1063293X211010542
     [Google Scholar]
  2. LeN. YamazakiK. QuachK.G. TruongD. SavvidesM. A multi-task contextual atrous residual network for brain tumor detection & segmentation.2020 25th International Conference on Pattern Recognition (ICPR)10-15 January 2021Milan, Italy202110.1109/ICPR48806.2021.9412414
     [Google Scholar]
  3. 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.202216709035737636
     [Google Scholar]
  4. ArabahmadiM. FarahbakhshR. RezazadehJ. Deep learning for smart Healthcare—A survey on brain tumor detection from medical imaging.Sensors.2022225196010.3390/s2205196035271115
     [Google Scholar]
  5. TiwariP. PantB. ElarabawyM.M. Abd-ElnabyM. MohdN. DhimanG. SharmaS. CNN based multiclass brain tumor detection using medical imaging.Comput. Intell. Neurosci.202220221810.1155/2022/183001035774437
     [Google Scholar]
  6. ObeidaviM.R. MaghooliK. Tumor detection in brain MRI using residual convolutional neural networks.2022 International Conference on Machine Vision and Image Processing (MVIP)23-24 February 2022Ahvaz, Iran, Islamic Republic of15202210.1109/MVIP53647.2022.9738767
     [Google Scholar]
  7. AminJ. SharifM. HaldoraiA. YasminM. Sundar NayakR. Brain tumor detection and classification using machine learning: A comprehensive survey.Complex & Intelligent Systems2021123
     [Google Scholar]
  8. BudatiA.K. KattaR.B. An automated brain tumor detection and classification from MRI images using machine learning techniques with IoT.Environ. Dev. Sustain.2022249105701058410.1007/s10668‑021‑01861‑8
     [Google Scholar]
  9. RazzaghiP. AbbasiK. ShiraziM. RashidiS. Multimodal brain tumor detection using multimodal deep transfer learning.Appl. Soft Comput.202212910963110.1016/j.asoc.2022.109631
     [Google Scholar]
  10. QureshiS.A. RazaS.E.A. HussainL. MalibariA.A. NourM.K. RehmanA. Al-WesabiF.N. HilalA.M. Intelligent ultra-light deep learning model for multi-class brain tumor detection.Appl. Sci.2022128371510.3390/app12083715
     [Google Scholar]
  11. Anaya-IsazaA. Mera-JiménezL. Data augmentation and transfer learning for brain tumor detection in magnetic resonance imaging.IEEE Access202210232172323310.1109/ACCESS.2022.3154061
     [Google Scholar]
  12. Sathies KumarT. ArunC. EzhumalaiP. An approach for brain tumor detection using optimal feature selection and optimized deep belief network.Biomed. Signal Process. Control20227310344010.1016/j.bspc.2021.103440
     [Google Scholar]
  13. JasmineH.J. ThirumuruganP. Performance analysis of meningioma brain tumor detection system using feature learning optimization and ANFIS classification method.J. Inst. Electron. Telecommun. Eng.20226821542155010.1080/03772063.2020.1844079
     [Google Scholar]
  14. AminJ. AnjumM.A. SharifM. JabeenS. KadryS. Moreno GerP. A new model for brain tumor detection using ensemble transfer learning and quantum variational classifier.Comput. Intell. Neurosci.2022202211310.1155/2022/323630535463245
     [Google Scholar]
  15. KaurD. SinghS. MansoorW. KumarY. VermaS. DashS. KoulA. Computational intelligence and metaheuristic techniques for brain tumor detection through IoMT-enabled MRI devices.Wirel. Commun. Mob. Comput.2022202212010.1155/2022/1519198
     [Google Scholar]
  16. MandleA.K. SahuS.P. GuptaG. Intelligent brain tumor detection system using deep learning technique.2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)01-03 March 2022Raipur, India202210.1109/ICPC2T53885.2022.9777073
     [Google Scholar]
  17. AlmadhounH.R. Abu-NaserS.S. Detection of brain tumor using deep learning.Int. J. Acad. Eng. Res.2022632947
     [Google Scholar]
  18. ModiyaP. VahoraS. Brain tumor detection using transfer learning with dimensionality reduction method.Int. J. Intell. Syst. Appl. Eng.2022102201206
     [Google Scholar]
  19. MamathaS.K. KrishnappaH.K. ShaliniN. Graph theory based segmentation of magnetic resonance images for brain tumor detection.Pattern Recognit. Image Anal.202232115316110.1134/S1054661821040167
     [Google Scholar]
  20. KanumuriC. RenuM.C.H. A survey: Brain tumor detection using MRI image with deep learning techniques.Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks: Real-time ApplicationsWiley202210.1002/9781119682554.ch6
     [Google Scholar]
  21. HalderT.K. SarkarK. MandalA. SarkarS. A novel histogram feature for brain tumor detection.Int. J. Inf. Technol.20221441883189210.1007/s41870‑022‑00917‑w
     [Google Scholar]
  22. KangJ. UllahZ. GwakJ. MRI-based brain tumor classification using ensemble of deep features and machine learning classifiers.Sensors.2021216222210.3390/s2106222233810176
     [Google Scholar]
  23. GuanY. AamirM. RahmanZ. AliA. AbroW.A. DayoZ.A. BhuttaM.S. HuZ. A framework for efficient brain tumor classification using MRI images.Math. Biosci. Eng.20211855790581510.3934/mbe.2021292
     [Google Scholar]
  24. RazaA. AyubH. KhanJ.A. A hybrid deep learning-based approach for brain tumor classification.Electronics.2022117114610.3390/electronics11071146
     [Google Scholar]
  25. Díaz-PernasF.J. Martínez-ZarzuelaM. Antón-RodríguezM. González-OrtegaD. A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network.Healthcare.20219215310.3390/healthcare9020153
     [Google Scholar]
  26. Abd El KaderI. XuG. ShuaiZ. SaminuS. JavaidI. Salim AhmadI. Differential deep convolutional neural network model for brain tumor classification.Brain Sci.202111335210.3390/brainsci1103035233801994
     [Google Scholar]
  27. KareemO.S. AL-SulaifanieA.K. HasanD.A. Segmenting and classifiying the brain tumor from MRI medical images based on machine learning algorithms: A review.Asian J. Res. Comput. Sci.2021102506110.9734/ajrcos/2021/v10i230239
     [Google Scholar]
  28. GurbinăM. LascuM. LascuD. Tumor detection and classification of MRI brain image using different wavelet transforms and support vector machines.2019 42nd International Conference on Telecommunications and Signal Processing (TSP)01-03 July 2019Budapest, Hungary201910.1109/TSP.2019.8769040
     [Google Scholar]
  29. KhanM.A. LaliI.U. RehmanA. IshaqM. SharifM. SabaT. ZahoorS. AkramT. Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection.Microsc. Res. Tech.201982690992210.1002/jemt.2323830801840
     [Google Scholar]
  30. KesavN. JibukumarM.G. Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN.J. King Saud Univ. - Comput. Inf. Sci.20223486229624210.1016/j.jksuci.2021.05.008
     [Google Scholar]
  31. AminJ. SharifM. RazaM. SabaT. AnjumM.A. Brain tumor detection using statistical and machine learning method.Comput. Methods Programs Biomed.2019177697910.1016/j.cmpb.2019.05.01531319962
     [Google Scholar]
  32. IrmakE. Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework.Iran. J. Sci. Technol. Trans. Electr. Eng.20214531015103610.1007/s40998‑021‑00426‑9
     [Google Scholar]
  33. SelvapandianA. ManivannanK. Fusion based Glioma brain tumor detection and segmentation using ANFIS classification.Comput. Methods Programs Biomed.2018166333810.1016/j.cmpb.2018.09.00630415716
     [Google Scholar]
  34. ZhaoC. XiangS. WangY. CaiZ. ShenJ. ZhouS. Di ZhaoW.S. GuoS. LiS. Context-aware network fusing transformer and V-Net for semi-supervised segmentation of 3D left atrium.Expert Syst. Appl.2022214119105
     [Google Scholar]
  35. WangW. ChenC. DingM. YuH. Transbts: Multimodal brain tumor segmentation using transformer.24th International ConferenceSeptember 27–October 1, 2021Strasbourg, France2021109119
     [Google Scholar]
  36. HuK. GanQ. ZhangY. DengS. XiaoF. HuangW. CaoC. GaoX. Brain tumor segmentation using multi-cascaded convolutional neural networks and conditional random field.IEEE Access20197926159262910.1109/ACCESS.2019.2927433
     [Google Scholar]
  37. ShoaibM. SayedN. YOLO object detector and inception-V3 convolutional neural network for improved brain tumor segmentation.Trait. du. Signal.202239137138010.18280/ts.390139
     [Google Scholar]
  38. JiangY. ZhangY. LinX. DongJ. ChengT. LiangJ. SwinBTS: A method for 3D multimodal brain tumor segmentation using swin transformer.Brain Sci.202212679710.3390/brainsci1206079735741682
     [Google Scholar]
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Automated Brain Tumor Detection using Ideal Shallow Neural Network with Artificial Jellyfish Optimization
20, e310723219285 (2024); https://doi.org/10.2174/1573405620666230731120924
/content/journals/cmim/10.2174/1573405620666230731120924
/content/journals/cmim/10.2174/1573405620666230731120924
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  • Article Type:     Research Article
Keyword(s): Artificial jellyfish optimization; Brain tumor; Centroid weighted segmentation; Gabor filtering; Grasshopper optimization algorithm methodology; Ideal shallow neural network; Magnetic resonance imaging; Multi-set feature extraction

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