Brain Tumor Detection Using Transfer Learning in Deep Learning

Sri Sai Meghana Alla; Kavitha Athota

doi:10.17485/IJST/v15i40.1307

Article

Brain Tumor Detection Using Transfer Learning in Deep Learning

VIEWS 1427
PDF 1460

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i40.1307

Year: 2022, Volume: 15, Issue: 40, Pages: 2093-2102

Original Article

Brain Tumor Detection Using Transfer Learning in Deep Learning

Sri Sai Meghana Alla¹, Kavitha Athota^2*

¹Department of Computer Science and Engineering, JNTUH College of Engineering, Hyderabad 500085, India
²Department of Computer Science and Engineering, JNTUH College of Engineering, Hyderabad 500085, India

*Corresponding Author
Email: [email protected]

Received Date:21 June 2022, Accepted Date:16 September 2022, Published Date:29 October 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background/Objectives: Magnetic resonance imaging (MRI) is widely used for tumor evaluation. However, MRI generates enormous data, making manual segmentation difficult in a reasonable amount of time, which limits the use of accurate measurements in clinical practice. Therefore, this study focuses on the automatic and reliable segmentation methods which are needed for early diagnosis of brain tumors. Methods: In this study, we used deep learning-based convolutional neural networks (CNNs) to extract features and automatically classify brain tumors based on MRI images. In addition to conventional CNNs, the application of transfer learning was investigated by using three types of CNNs (Inception-V3, VGG-16, and VGG-19) to achieve reasonable accuracy, with fine tuning of the final layers to improve the accuracy of the models. Findings: The results show that applying transfer learning to a CNN achieves high accuracy in less time and with a smaller dataset. VGG- 19 achieves 97 % accuracy, and VGG-16 achieves 96 % accuracy, which is better than the accuracy of Inception-V3 (89 %). Our proposed model using CNNs with transfer learning provides more robust automatic and reliable segmentation methods. Novelty: The novelty of this work is the use of Transfer Learning in conjunction with deep learning-based CNNs, as Transfer Learning provides a novel technique to analyse data with few annotations by transferring information from the source domain to the target domain.

Keywords: Brain tumor detection; Convolutional Neural Networks; Malignant; Benign; Transfer Learning VGGNET; InceptionV3; MRI

References

Meir EGV, Hadjipanayis CG, Norden AD, Shu HK, Wen PY, Olson JJ. Exciting New Advances in Neuro-Oncology: The Avenue to a Cure for Malignant Glioma. CA: A Cancer Journal for Clinicians. 2010;60(3):166–193. Available from: https://doi.org/10.3322/caac.20069
Deangelis LM. Brain tumours. New England Journal of Medicine. 2001;344(2):114–123. Available from: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.465.3127&rep=rep1&type=pdf
Scegedy C, Liu W, Jia Y. Going Deeper With Convolutions in Proceedings of the IEEE Conference on Computer Vision and Pattern. . Available from: https://doi.org/10.48550/arXiv.1409.4842
Prakash RM, Kumari RSS. Classification of MR Brain Images for Detection of Tumor with Transfer Learning from Pre-trained CNN Models. In: 2019 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET). (pp. 508-511) IEEE. 2019.
Abdalla HEM, Esmail MY. Brain Tumor Detection by using Artificial Neural Network. In: 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). (pp. 1-6) IEEE. 2018.
Pereira S, Pinto A, Alves V, Silva CA. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. IEEE Transactions on Medical Imaging. 2016;35(5):1240–1251. Available from: https://doi.org/10.1109/TMI.2016.2538465
Abd-Ellah MK, Awad AI, Khalaf AA, Hamed HF. Two phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP Journal on Image and Video Processing. 2018;2018(1). Available from: https://doi.org/10.1186/s13640-018-0332-4
Noreen N, Palaniappan S, Qayyum A, Ahmad I, Imran M, Shoaib M. A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor. IEEE Access. 2020;8:55135–55144. Available from: https://doi.org/10.1109/ACCESS.2020.2978629
Khan HA, Jue W, Mushtaq M, Mushtaq MU. Brain tumour classification in MRI image using convolutional neural network. Mathematical Biosciences and Engineering. 2020;17(5):6203–6216. Available from: https://doi.org/10.3934/mbe.2020328
Amin J, Sharif M, Haldorai A, Yasmin M, Nayak RS. Brain tumor detection and classification using machine learning: a comprehensive survey. Complex & Intelligent Systems. 2022;8(4):3161–3183. Available from: https://doi.org/10.1007/s40747-021-00563-y
Srivastava N, Hinton GE, Sutskever I. A simple way to prevent neural networks from over fitting. Journal of Machine Learning Research. . Available from: https://www.jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
Dvorák P, Menze B. Structured prediction with convolutional neural networks for multimodal brain tumour segmentation, MICCAI Multimodal Brain Tumour Segmentation Challenge (BraTS) 2015;p. 13–24. Available from: http://people.csail.mit.edu/menze/papers/dvorak_15_cnnTumor.pdf
Irsheidat S, Duwairi R. Brain Tumor Detection Using Artificial Convolutional Neural Networks. In: 2020 11th International Conference on Information and Communication Systems (ICICS). (pp. 197-203) IEEE. 2020.
Sravya V, Malathi S. Survey on Brain Tumor Detection using Machine Learning and Deep Learning. 2021 International Conference on Computer Communication and Informatics (ICCCI). 2021;p. 1–3. Available from: https://doi.org/10.1109/ICCCI50826.2021.9457019
Dipu NM, Shohan SA, Salam KMA. Deep Learning Based Brain Tumor Detection and Classification. 2021 International Conference on Intelligent Technologies (CONIT). 2021;p. 1–6. Available from: https://doi.org/10.1109/CONIT51480.2021.9498384
Gaikwad S, Patel S, Shetty A. Brain Tumor Detection: An Application Based on Machine Learning. 2021 2nd International Conference for Emerging Technology (INCET). 2021;p. 1–4. Available from: https://dx.doi.org/10.1109/INCET51464.2021.9456347
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. Computer Vision and Pattern Recognition. . Available from: https://doi.org/10.48550/arXiv.1512.00567
Varshney P. VGGNet-16 Architecture: A Complete Guide. Computer Vision and Pattern Recognition. . Available from: https://www.kaggle.com/blurredmachine/vggnet-16-architecture-a-complete-guide
Ullah N, Khan JA, Khan MS, Khan WA, Hassan I, Obayya M, et al. An Effective Approach to Detect and Identify Brain Tumors Using Transfer Learning. Applied Sciences. 2022;12(11):5645. Available from: https://doi.org/10.3390/app12115645
Khalid El Fazazy, Mohamed Adnane Mahraz, Hamid Tairi. Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning (BML 2021). ;p. 566–571.
Amin J, Anjum MA, Sharif M, Jabeen S, Kadry S, Ger PM. A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier. Computational Intelligence and Neuroscience. 2022;2022:1–13. Available from: https://doi.org/10.1155/2022/3236305
Raza A, Ayub H, Khan JA, Ahmad IS, Salama AS, Daradkeh YI, et al. A Hybrid Deep Learning-Based Approach for Brain Tumor Classification. Electronics. 2022;11(7):1146. Available from: https:// doi.org/10.3390/electronics11071146
Charulatha G, Balaji B. Mobile Application to Detect Brain Tumor Using Transfer Learning”. Journal of Science, Computing and Engineering Research. 2022;3(2):247–252. Available from: https://doi.org/10.46379/jscer.2022.030204

Copyright

© 2022 Alla & Athota. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)