Brain Tumor Classification for MR Images Using Transfer Learning and EfficientNetB3
Abstract
Brain tumors are one of the diseases that take many lives in the world, moreover, brain tumors have various types. In the medical world, it has an technology called Magnetic Resonance Imaging (MRI) which functions to see the inside of the human body using a magnetic field. CNN is designed to determine features adaptively using backpropagation by applying layers such as convolutional layers, and pooling layers. This study aims to optimize and increase the accuracy of the classification of brain tumor MRI images using the Convolutional Neural Network (CNN) EfficientNet model. The proposed system consists of two main steps. First, preprocessing images using various methods then classifying images that have been preprocessed using CNN. This study used 3064 images containing three types of brain tumors (gliomata, meningiomas, and pituitary). This study resulted in an accuracy of 98.00%, a precision of 96.00%, and an average recall of 97.00% using the model that the researcher applied.
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References
U. R. Acharya et al., “Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques,” J Med Syst, vol. 43, no. 9, Sep. 2019, doi: 10.1007/s10916-019-1428-9.
V. Rajinikanth, S. Kadry, and Y. Nam, “Convolutional-neural-network assisted segmentation and svm classification of brain tumor in clinical mri slices,” Information Technology and Control, vol. 50, no. 2, pp. 342–356, 2021, doi: 10.5755/j01.itc.50.2.28087.
M. Jaganjac, M. Cindrić, A. Jakovčević, K. Žarković, and N. Žarković, “Lipid peroxidation in brain tumors,” Neurochemistry International, vol. 149. Elsevier Ltd, Oct. 01, 2021. doi: 10.1016/j.neuint.2021.105118.
Z. N. K. Swati et al., “Brain tumor classification for MR images using transfer learning and fine-tuning,” Computerized Medical Imaging and Graphics, vol. 75, pp. 34–46, Jul. 2019, doi: 10.1016/j.compmedimag.2019.05.001.
R. Reda, A. Zanza, A. Mazzoni, A. Cicconetti, L. Testarelli, and D. di Nardo, “An update of the possible applications of magnetic resonance imaging (Mri) in dentistry: A literature review,” Journal of Imaging, vol. 7, no. 5. MDPI AG, May 01, 2021. doi: 10.3390/jimaging7050075.
D. di Nardo, G. Gambarini, S. Capuani, and L. Testarelli, “Nuclear Magnetic Resonance Imaging in Endodontics: A Review,” Journal of Endodontics, vol. 44, no. 4. Elsevier Inc., pp. 536–542, Apr. 01, 2018. doi: 10.1016/j.joen.2018.01.001.
S. J. Chockattu, D. B. Suryakant, and S. Thakur, “Unwanted effects due to interactions between dental materials and magnetic resonance imaging: a review of the literature,” Restor Dent Endod, vol. 43, no. 4, 2018, doi: 10.5395/rde.2018.43.e39.
Y. Azhar, Moch. C. Mustaqim, and A. E. Minarno, “Ensemble convolutional neural network for robust batik classification,” IOP Conf Ser Mater Sci Eng, vol. 1077, no. 1, p. 012053, Feb. 2021, doi: 10.1088/1757-899x/1077/1/012053.
R. S, S. A. K, and N. Subramanyam, “Transfer Learning using Neural Ordinary Differential Equations,” Jan. 2020, [Online]. Available: http://arxiv.org/abs/2001.07342
East-West University, Institute of Electrical and Electronics Engineers, Institute of Electrical and Electronics Engineers. Bangladesh Section, and IEEE Robotics and Automation Society. Bangladesh Chapter, 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT 2019) : May 3-5, 2019, Dhaka, Bangladesh.
A. R. Khan, S. Khan, M. Harouni, R. Abbasi, S. Iqbal, and Z. Mehmood, “Brain tumor segmentation using K-means clustering and deep learning with synthetic data augmentation for classification,” Microsc Res Tech, vol. 84, no. 7, pp. 1389–1399, Jul. 2021, doi: 10.1002/jemt.23694.
D. R. Nayak, N. Padhy, P. K. Mallick, M. Zymbler, and S. Kumar, “Brain Tumor Classification Using Dense Efficient-Net,” Axioms, vol. 11, no. 1, Jan. 2022, doi: 10.3390/axioms11010034.
S. Reddy, K. Tatiparti, S. Sau, and A. K. Iyer, “Recent advances in nano delivery systems for blood-brain barrier (BBB) penetration and targeting of brain tumors,” Drug Discovery Today, vol. 26, no. 8. Elsevier Ltd, pp. 1944–1952, Aug. 01, 2021. doi: 10.1016/j.drudis.2021.04.008.
East-West University, Institute of Electrical and Electronics Engineers, Institute of Electrical and Electronics Engineers. Bangladesh Section, and IEEE Robotics and Automation Society. Bangladesh Chapter, 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT 2019) : May 3-5, 2019, Dhaka, Bangladesh.
S. Kalvankar, H. Pandit, and P. Parwate, “Galaxy Morphology Classification using EfficientNet Architectures,” Aug. 2020, [Online]. Available: http://arxiv.org/abs/2008.13611
S. S. Keh, “Semi-Supervised Noisy Student Pre-training on EfficientNet Architectures for Plant Pathology Classification,” Dec. 2020, [Online]. Available: http://arxiv.org/abs/2012.00332
Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, “Plant leaf disease classification using EfficientNet deep learning model,” Ecol Inform, vol. 61, Mar. 2021, doi: 10.1016/j.ecoinf.2020.101182.
M. Chetoui, M. A. Akhloufi, and S. Member IEEE, Explainable Diabetic Retinopathy using EfficientNET*. 2020. doi: 10.0/Linux-x86_64.
M. Tan and Q. v Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.”
J. Fan, J. Lee, and Y. Lee, “A transfer learning architecture based on a support vector machine for histopathology image classification,” Applied Sciences (Switzerland), vol. 11, no. 14, Jul. 2021, doi: 10.3390/app11146380.
H. Alhichri, A. S. Alswayed, Y. Bazi, N. Ammour, and N. A. Alajlan, “Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model with Attention,” IEEE Access, vol. 9, pp. 14078–14094, 2021, doi: 10.1109/ACCESS.2021.3051085.
K. Weiss, T. M. Khoshgoftaar, and D. D. Wang, “A survey of transfer learning,” J Big Data, vol. 3, no. 1, Dec. 2016, doi: 10.1186/s40537-016-0043-6.
East-West University, Institute of Electrical and Electronics Engineers, Institute of Electrical and Electronics Engineers. Bangladesh Section, and IEEE Robotics and Automation Society. Bangladesh Chapter, 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT 2019) : May 3-5, 2019, Dhaka, Bangladesh.
N. Abiwinanda, M. Hanif, S. T. Hesaputra, A. Handayani, and T. R. Mengko, “Brain tumor classification using convolutional neural network,” in IFMBE Proceedings, 2019, vol. 68, no. 1, pp. 183–189. doi: 10.1007/978-981-10-9035-6_33.
B. Zoph, V. Vasudevan, J. Shlens, and Q. v. Le, “Learning Transferable Architectures for Scalable Image Recognition,” Jul. 2017, [Online]. Available: http://arxiv.org/abs/1707.07012
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