Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Medical Imaging
  4. Volume 20, Issue 1
  5. Article
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Purpose

Brain tumour detection and classification require trained radiologists for efficient diagnosis. The proposed work aims to build a Computer Aided Diagnosis (CAD) tool to automate brain tumour detection using Machine Learning (ML) and Deep Learning (DL) techniques.

Materials and Methods

Magnetic Resonance Image (MRI) collected from the publicly available Kaggle dataset is used for brain tumour detection and classification. Deep features extracted from the global pooling layer of Pretrained Resnet18 network are classified using 3 different ML Classifiers, such as Support vector Machine (SVM), K-Nearest Neighbour (KNN), and Decision Tree (DT). The above classifiers are further hyperparameter optimised using Bayesian Algorithm (BA) to enhance the performance. Fusion of features extracted from shallow and deep layers of the pretrained Resnet18 network followed by BA-optimised ML classifiers is further used to enhance the detection and classification performance. The confusion matrix derived from the classifier model is used to evaluate the system's performance. Evaluation metrics, such as accuracy, sensitivity, specificity, precision, F1 score, Balance Classification Rate (BCR), Mathews Correlation Coefficient (MCC) and Kappa Coefficient (Kp), are calculated.

Results

Maximum accuracy, sensitivity, specificity, precision, F1 score, BCR, MCC, and Kp of 99.11%, 98.99%, 99.22%, 99.09%, 99.09%, 99.10%, 98.21%, 98.21%, respectively, were obtained for detection using fusion of shallow and deep features of Resnet18 pretrained network classified by BA optimized SVM classifier. Feature fusion performs better for classification task with accuracy, sensitivity, specificity, precision, F1 score, BCR, MCC and Kp of 97.31%, 97.30%, 98.65%, 97.37%, 97.34%, 97.97%, 95.99%, 93.95%, respectively.

Conclusion

The proposed brain tumour detection and classification framework using deep feature extraction from Resnet 18 pretrained network in conjunction with feature fusion and optimised ML classifiers can improve the system performance. Henceforth, the proposed work can be used as an assistive tool to aid the radiologist in automated brain tumour analysis and treatment.

© 2024 The Author(s). Published by Bentham Open.
© 2024 The Author(s). Published by Bentham Open. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
Loading

Article metrics loading...

/content/journals/cmir/10.2174/1573405620666230328092218
2023-05-23
2024-11-23

  • From This Site

    /content/journals/cmir/10.2174/1573405620666230328092218
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/cmir/20/1/CMIM-20-e280323215032.html?itemId=/content/journals/cmir/10.2174/1573405620666230328092218&mimeType=html&fmt=ahah

References

  1. KV C King GG. Brain tumour classification: A comprehensive systematic review on various constraints.Comput. Methods Biomech. Biomed. Eng. Imaging Vis.202213
     [Google Scholar]
  2. SharmaR. Aashima NandaM. FronterreC. SewaguddeP. SsentongoA.E. YenneyK. ArhinN.D. OhJ. Amponsah-ManuF. SsentongoP. Mapping cancer in africa: A comprehensive and comparable characterization of 34 cancer types using estimates from GLOBOCAN 2020.Front. Public Health20221083983510.3389/fpubh.2022.83983535548083
     [Google Scholar]
  3. ArabahmadiM. FarahbakhshR. RezazadehJ. Deep learning for smart healthcare-A survey on brain tumour detection from medical imaging.Sensors2022225196010.3390/s2205196035271115
     [Google Scholar]
  4. MzoughiH. NjehI. SlimaM.B. HamidaA.B. Review of computer aided-diagnosis (CAD) systems for MRI gliomas brain tumours explorations based on machine learning and deep learning.In2022 6th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)202216
     [Google Scholar]
  5. NazirM. ShakilS. KhurshidK. Role of deep learning in brain tumour detection and classification (2015 to 2020): A review.Comput. Med. Imaging Graph.20219110194010.1016/j.compmedimag.2021.10194034293621
     [Google Scholar]
  6. DeviP.R. VemuruS. Brain tumour detection with feature extraction and tumour cell classification model using machine learning–A survey.2022 International Conference on Electronics and Renewable Systems (ICEARS)202212501259IEEE10.1109/ICEARS53579.2022.9752080
     [Google Scholar]
  7. BharathiM. KumarS.A. SasikalaS. Deep learning architectures for improving effectiveness of Covid detection–A pilot study.2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA)202115IEEE.10.1109/ICAECA52838.2021.9675714
     [Google Scholar]
  8. ZhuangF. QiZ. DuanK. XiD. ZhuY. ZhuH. XiongH. HeQ. A comprehensive survey on transfer learning.Proc. IEEE20211091437610.1109/JPROC.2020.3004555
     [Google Scholar]
  9. SethyP.K. BeheraS.K. A data constrained approach for brain tumour detection using fused deep features and SVM.Multimedia Tools Appl.20218019287452876010.1007/s11042‑021‑11098‑2
     [Google Scholar]
  10. RagabD.A. AttallahO. SharkasM. RenJ. MarshallS. A framework for breast cancer classification using Multi-DCNNs.Comput. Biol. Med.202113110424510.1016/j.compbiomed.2021.10424533556893
     [Google Scholar]
  11. SwatiZ.N.K. ZhaoQ. KabirM. AliF. AliZ. AhmedS. LuJ. Brain tumour classification for MR images using transfer learning and fine-tuning.Comput. Med. Imaging Graph.201975344610.1016/j.compmedimag.2019.05.00131150950
     [Google Scholar]
  12. VankdothuR. HameedM.A. FatimaH. A brain tumour identification and classification using deep learning based on CNN-LSTM method.Comput. Electr. Eng.202210110796010.1016/j.compeleceng.2022.107960
     [Google Scholar]
  13. DasA. MohapatraS.K. MohantyM.N. Design of deep ensemble classifier with fuzzy decision method for biomedical image classification.Appl. Soft Comput.202211510817810.1016/j.asoc.2021.108178
     [Google Scholar]
  14. AminJ. AnjumM.A. SharifM. JabeenS. KadryS. Moreno GerP. A new model for brain tumour detection using ensemble transfer learning and quantum variational classifier.Comput. Intell. Neurosci.2022202211310.1155/2022/323630535463245
     [Google Scholar]
  15. RaniS. GhaiD. KumarS. KantipudiM.V.V.P. AlharbiA.H. UllahM.A. Efficient 3D alexnet architecture for object recognition using syntactic patterns from medical images.Comput. Intell. Neurosci.2022202211910.1155/2022/788292435634047
     [Google Scholar]
  16. KibriyaH. AminR. AlshehriA.H. MasoodM. AlshamraniS.S. AlshehriA. A novel and effective brain tumour classification model using deep feature fusion and famous machine learning classifiers.Comput. Intell. Neurosci.2022202211510.1155/2022/789766935378808
     [Google Scholar]
  17. 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.2022202211710.1155/2022/833083335633922
     [Google Scholar]
  18. AlsaifH. GuesmiR. AlshammariB.M. HamrouniT. GuesmiT. AlzamilA. BelguesmiL. A novel data augmentation-based brain tumour detection using convolutional neural network.Appl. Sci.2022128377310.3390/app12083773
     [Google Scholar]
  19. ShwethaV. MadhaviC.R. Nagendra KumarM. Classification of brain tumours using hybridized convolutional neural network in brain MRI images.International Journal of Circuits, Systems and Signal Processing.202216561570
     [Google Scholar]
  20. NayakD.R. PadhyN. MallickP.K. BagalD.K. KumarS. Brain tumour classification using noble deep learning approach with parametric optimization through metaheuristics approaches.Computers20221111010.3390/computers11010010
     [Google Scholar]
  21. LatifG. Ben BrahimG. IskandarD.N.F.A. BasharA. AlghazoJ. Glioma tumours’ classification using deep-neural-network-based features with SVM classifier.Diagnostics2022124101810.3390/diagnostics1204101835454066
     [Google Scholar]
  22. IrmakE. Multi-classification of brain tumour 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]
  23. KibriyaH. MasoodM. NawazM. NazirT. Multiclass classification of brain tumours using a novel CNN architecture.Multimedia Tools Appl.20228121298472986310.1007/s11042‑022‑12977‑y
     [Google Scholar]
  24. KangJ. UllahZ. GwakJ. Mri-based brain tumour classification using ensemble of deep features and machine learning classifiers.Sensors2021216222210.3390/s2106222233810176
     [Google Scholar]
  25. AlanaziM.F. AliM.U. HussainS.J. ZafarA. MohatramM. IrfanM. AlRuwailiR. AlruwailiM. AliN.H. AlbarrakA.M. Brain tumour/mass classification framework using magnetic-resonance-imaging-based isolated and developed transfer deep-learning model.Sensors202222137210.3390/s2201037235009911
     [Google Scholar]
  26. ZahoorM.M. QureshiS.A. BibiS. KhanS.H. KhanA. GhafoorU. BhuttaM.R. A new deep hybrid boosted and ensemble learning-based brain tumour analysis using MRI.Sensors2022227272610.3390/s2207272635408340
     [Google Scholar]
  27. SeethaJ. RajaS.S. Brain tumour classification using convolutional neural networks.Biomed. Pharmacol. J.20181131457146110.13005/bpj/1511
     [Google Scholar]
  28. DasS. AranyaO.R. LabibaN.N. Brain tumour classification using convolutional neural network.2019 1st international conference on advances in science, engineering and robotics technology (ICASERT).20191510.1109/ICASERT.2019.8934603
     [Google Scholar]
  29. VashishthaP. Brain tumour classification.2021Available From: https://www.kaggle.com/code/purvitsharma/brain-tumour-classification-98-4-accuracy/data
  30. BhuvajiS. KadamA. BhumkarP. DedgeS. KanchanS. Brain tumour classification (MRI).202010.34740/KAGGLE/DSV/1183165
     [Google Scholar]
  31. HeK. ZhangX. RenS. SunJ. Deep residual learning for image recognition.Proceedings of the IEEE conference on computer vision and pattern recognition2016770778
     [Google Scholar]
  32. PolatÖ. GüngenC. Classification of brain tumours from MR images using deep transfer learning.J. Supercomput.20217777236725210.1007/s11227‑020‑03572‑9
     [Google Scholar]
  33. AyyachamyS. AlexV. KhenedM. KrishnamurthiG. Medical image retrieval using Resnet-18.Medical imaging. Imaging informatics for healthcare, research, and applications.Int Soc Opt Photo2019Vol. 109541095410
     [Google Scholar]
  34. Sannasi ChakravarthyS.R. RajaguruH. Automatic detection and classification of mammograms using improved extreme learning machine with deep learning.IRBM2022431496110.1016/j.irbm.2020.12.004
     [Google Scholar]
/content/journals/cmir/10.2174/1573405620666230328092218
Loading
Automated Brain Tumour Detection and Classification using Deep Features and Bayesian Optimised Classifiers
Curr. Med. Imaging 20, E280323215032 (2024); https://doi.org/10.2174/1573405620666230328092218
/content/journals/cmir/10.2174/1573405620666230328092218
/content/journals/cmir/10.2174/1573405620666230328092218
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Brain tumour; Classification; Deep learning; Detection; Hyper parameter tuning; Resnet 18; Transfer learning

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test