Evaluation of transfer learning for brain tumor detection in medical images
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1302Keywords:
Transfer Learning, Convolutional Neural Network, Brain TumorAbstract
Objective: With the increased feasibility of applying convolutional neural networks (CNNs), the goal was to evaluate the use of this technology for detecting brain tumors in computerized magnetic resonance images. Method: Two distinct CNN models were developed, one using transfer learning and the other without, to classify the occurrence of brain tumors. Results: The model without transfer learning achieved an accuracy of 99.67%, with a sensitivity of 100% and specificity of 99.34%. The model using transfer learning achieved an accuracy of 98%, with a sensitivity of 98.32% and specificity of 97.69%. Conclusion: This study highlights the efficacy of CNNs in detecting brain tumors, suggesting the use of intelligent systems as auxiliary tools.
References
Rethemiotaki I. Brain tumour detection from magnetic resonance imaging using convolutional neural networks. Contemp Oncol [Internet]. 2023 [cited 2024 Apr 21];27(4):230. Available from: /pmc/articles/PMC10883197/
Fritz A, Percy C, Jack A. International Classification of Diseases for Oncology. Third. Vol. 1. Switzerland: World Health Organization; 2000. 0–222 p.
Tandel GS, Biswas M, Kakde OG, Tiwari A, Suri HS, Turk M, et al. A review on a deep learning perspective in brain cancer classification. Cancers (Basel) [Internet]. 2019 Jan 18 [cited 2021 Dec 12];11(1):111. Available from: https://www.mdpi.com/2072-6694/11/1/111/htm
Kang J, Ullah Z, Gwak J. Mri-based brain tumor classification using ensemble of deep features and machine learning classifiers. Sensors [Internet]. 2021 Mar 22 [cited 2021 Dec 12];21(6):1–21. Available from: https://www.mdpi.com/1424-8220/21/6/2222/htm
Patel AP, Fisher JL, Nichols E, Abd-Allah F, Abdela J, Abdelalim A, et al. Global, regional, and national burden of brain and other CNS cancer, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol [Internet]. 2019 Apr 1 [cited 2024 Apr 22];18(4):376–93. Available from: https://pubmed.ncbi.nlm.nih.gov/30797715/
Naseer A, Yasir T, Azhar A, Shakeel T, Zafar K. Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI. Int J Biomed Imaging. 2021;2021.
Fernandes SL, Tanik UJ, Rajinikanth V, Karthik KA. A reliable framework for accurate brain image examination and treatment planning based on early diagnosis support for clinicians. Neural Comput Appl [Internet]. 2020 Oct 1 [cited 2024 Apr 22];32(20):15897–908. Available from: https://link.springer.com/article/10.1007/s00521-019-04369-5
Naseer A, Yasir T, Azhar A, Shakeel T, Zafar K. Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI. Int J Biomed Imaging. 2021;2021.
Naseer A, Zafar K. Comparative Analysis of Raw Images and Meta Feature based Urdu OCR using CNN and LSTM. International Journal of Advanced Computer Science and Applications. 2018;9(1):419–24.
Naseer A, Zafar K. Meta features-based scale invariant OCR decision making using LSTM-RNN. Comput Math Organ Theory [Internet]. 2019 Jun 1 [cited 2024 Apr 22];25(2):165–83. Available from: https://link.springer.com/article/10.1007/s10588-018-9265-9
Papageorgiou V. Brain tumor detection based on features extracted and classified using a low-complexity neural network. Traitement du Signal. 2021 Jun 1;38(3):547–54.
Papageorgiou EI, Spyridonos PP, Glotsos DT, Stylios CD, Ravazoula P, Nikiforidis GN, et al. Brain tumor characterization using the soft computing technique of fuzzy cognitive maps. Applied Soft Computing Journal. 2008 Jan 1;8(1):820–8.
Deepak S, Ameer PM. Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med [Internet]. 2019 Aug 1 [cited 2024 Apr 28];111. Available from: https://pubmed.ncbi.nlm.nih.gov/31279167/
Wu M, Liu Q, Yan C, Sen G. Multi-Classification of Brain Tumors on Magnetic Resonance Images Using an Ensemble of Pre-Trained Convolutional Neural Networks. Curr Med Imaging [Internet]. 2022 Apr 18 [cited 2024 Apr 28];19(1):65–76. Available from: https://pubmed.ncbi.nlm.nih.gov/35430973/
Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, et al. Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph [Internet]. 2019 Jul 1 [cited 2024 Apr 28];75:34–46. Available from: https://pubmed.ncbi.nlm.nih.gov/31150950/
Kang J, Ullah Z, Gwak J. Mri-based brain tumor classification using ensemble of deep features and machine learning classifiers. Sensors. 2021 Mar 22;21(6):1–21.
Br35H :: Detecção de tumor cerebral 2020 | Kaggle [Internet]. [cited 2021 Dec 12]. Available from: https://www.kaggle.com/ahmedhamada0/brain-tumor-detection
Papageorgiou V. Brain tumor detection based on features extracted and classified using a low-complexity neural network. Traitement du Signal. 2021 Jun 1;38(3):547–54.
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. Int J Comput Vis [Internet]. 2016 Oct 7 [cited 2024 May 27];128(2):336–59. Available from: http://arxiv.org/abs/1610.02391
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Submission of a paper to Journal of Health Informatics is understood to imply that it is not being considered for publication elsewhere and that the author(s) permission to publish his/her (their) article(s) in this Journal implies the exclusive authorization of the publishers to deal with all issues concerning the copyright therein. Upon the submission of an article, authors will be asked to sign a Copyright Notice. Acceptance of the agreement will ensure the widest possible dissemination of information. An e-mail will be sent to the corresponding author confirming receipt of the manuscript and acceptance of the agreement.
