(Daffodil International University, Bangladesh)(2) Md. Hasan Imam Bijoy
(Daffodil International University, Bangladesh)(3) Sabina Yesmin
(Daffodil International University, Bangladesh)(4) Imran Mahmud
(Daffodil International University, Bangladesh)(5) Md. Jueal Mia
(Daffodil International University, Bangladesh)(6) Khalid Been Badruzzaman Biplob
(Daffodil International University, Bangladesh)*corresponding author
AbstractAbnormal brain tissue or cell growth is known as a brain tumor. One of the body's most intricate organs is the brain, where billions of cells work together. As a head tumor grows, the brain suffers damage due to its increasingly dense core. Magnetic resonance imaging, or MRI, is a type of medical imaging that enables radiologists to view the inside of body structures without the need for surgery. The image-based medical diagnosis expert system is crucial for a brain tumor patient. In this study, we combined two Magnetic Resonance Imaging (MRI)-based image datasets from Figshare and Kaggle to identify brain tumor MRI using a variety of convolutional neural network designs. To achieve competitive performance, we employ several data preprocessing techniques, such as resizing and enhancing contrast. The image augmentation techniques (E.g., rotated, width shifted, height shifted, shear shifted, and horizontally flipped) are used to increase data size, and five pre-trained models employed, including VGG-16, VGG-19, ResNet-50, Xception, and Inception-V3. The model with the highest accuracy, ResNet-50, performs at 96.76 percent. The model with the highest precision overall is Inception V3, with a precision score of 98.83 percent. ResNet-50 performs at 96.96% for F1-Score. The prominent accuracy of the implemented model, i.e., ResNet-50, compared with several earlier studies to validate the consequence of this introspection. The outcome of this study can be used in the medical diagnosis of brain tumors with an MRI-based expert system.
KeywordsBrain Tumor; MRI Images; VGG16; VGG19; ResNet50.
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DOIhttps://doi.org/10.26555/ijain.v9i2.872 |
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References
[1] A. M. Rauschecker et al., “Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI,” Radiology, vol. 295, no. 3, pp. 626–637, Jun. 2020, doi: 10.1148/radiol.2020190283.
[2] S. Pereira, A. Pinto, V. Alves, and C. A. Silva, “Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1240–1251, May 2016, doi: 10.1109/TMI.2016.2538465.
[3] R. R. Laddha and S. A. Ladhake, “A Review on Brain Tumor Detection Using Segmentation And Threshold Operations,” Int. J. Comput. Sci. Inf. Technol., vol. 5, no. 1, pp. 607–611, 2014. [Online]. Available at : https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=a7cb1e37a7d0306bb174898fca4ba52a52cb280c
[4] “Brain Tumors - Classifications, Symptoms, Diagnosis and Treatments,” American Association of Neorological Surgeons. Accessed Apr. 05, 2022. [Online]. Available at: https://www.aans.org/en/Patients/Neurosurgical-Conditions-and-Treatments/Brain-Tumors.
[5] R. Krishna and T. Menzies, “Bellwethers: A Baseline Method for Transfer Learning,” IEEE Trans. Softw. Eng., vol. 45, no. 11, pp. 1081–1105, Nov. 2019, doi: 10.1109/TSE.2018.2821670.
[6] D. Theckedath and R. R. Sedamkar, “Detecting Affect States Using VGG16, ResNet50 and SE-ResNet50 Networks,” SN Comput. Sci., vol. 1, no. 2, p. 79, Mar. 2020, doi: 10.1007/s42979-020-0114-9.
[7] F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, vol. 2017-Janua, pp. 1800–1807, doi: 10.1109/CVPR.2017.195.
[8] A. Y. Saleh, C. K. Chin, V. Penshie, and H. R. H. Al-Absi, “Lung cancer medical images classification using hybrid CNN-SVM,” Int. J. Adv. Intell. Informatics, vol. 7, no. 2, p. 151, Jul. 2021, doi: 10.26555/ijain.v7i2.317.
[9] M. Shatara et al., “EPCT-07. Updated report on the pilot study of using MRI-guided laser heat ablation to induce disruption of the peritumoral blood brain barrier to enhance deliver and efficacy of treatment of pediatric brain tumors,” Neuro. Oncol., vol. 24, no. Supplement_1, pp. i37–i37, Jun. 2022, doi: 10.1093/neuonc/noac079.135.
[10] S. Deepak and P. M. Ameer, “Brain tumor classification using deep CNN features via transfer learning,” in Computers in Biology and Medicine, Aug. 2019, vol. 111, p. 103345, doi: 10.1016/j.compbiomed.2019.103345.
[11] M. Talo, U. B. Baloglu, Ö. Yıldırım, and U. Rajendra Acharya, “Application of deep transfer learning for automated brain abnormality classification using MR images,” in Cognitive Systems Research, May 2019, vol. 54, pp. 176–188, doi: 10.1016/j.cogsys.2018.12.007.
[12] S. Ahuja, B. K. Panigrahi, and T. Gandhi, “Transfer Learning Based Brain Tumor Detection and Segmentation using Superpixel Technique,” in 2020 International Conference on Contemporary Computing and Applications (IC3A), Feb. 2020, pp. 244–249, doi: 10.1109/IC3A48958.2020.233306.
[13] M. A. Khan et al., “Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists,” Diagnostics, vol. 10, no. 8, p. 565, Aug. 2020, doi: 10.3390/diagnostics10080565.
[14] T. Kaur and T. K. Gandhi, “Deep convolutional neural networks with transfer learning for automated brain image classification,” Mach. Vis. Appl., vol. 31, no. 3, p. 20, Mar. 2020, doi: 10.1007/s00138-020-01069-2.
[15] C. Srinivas et al., “Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images,” in Journal of Healthcare Engineering, Mar. 2022, vol. 2022, pp. 1–17, doi: 10.1155/2022/3264367.
[16] Xiaoling Xia, Cui Xu, and Bing Nan, “Inception-v3 for flower classification,” in 2017 2nd International Conference on Image, Vision and Computing (ICIVC), Jun. 2017, pp. 783–787, doi: 10.1109/ICIVC.2017.7984661.
[17] A. K. Bitto and I. Mahmud, “Multi categorical of common eye disease detect using convolutional neural network: a transfer learning approach,” Bull. Electr. Eng. Informatics, vol. 11, no. 4, pp. 2378–2387, Aug. 2022, doi: 10.11591/eei.v11i4.3834.
[18] A. Wadhwa, A. Bhardwaj, and V. Singh Verma, “A review on brain tumor segmentation of MRI images,” in Magnetic Resonance Imaging, Sep. 2019, vol. 61, pp. 247–259, doi: 10.1016/j.mri.2019.05.043.
[19] “Brain Tumor MRI Dataset,” Kaggle. Accessed Apr. 24, 2022. [Online]. Available at: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset .
[20] J. Mia, H. I. Bijoy, S. Uddin, and D. M. Raza, “Real-Time Herb Leaves Localization and Classification Using YOLO,” in 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Jul. 2021, pp. 1–7, doi: 10.1109/ICCCNT51525.2021.9579718.
[21] A. W. Reza, J. F. Sorna, M. M. U. Rashel, and M. M. A. Shibly, “ModCOVNN: a convolutional neural network approach in COVID-19 prognosis,” Int. J. Adv. Intell. Informatics, vol. 7, no. 2, p. 125, Apr. 2021, doi: 10.26555/ijain.v7i2.604.
[22] H. Ali Khan, W. Jue, M. Mushtaq, and M. Umer Mushtaq, “Brain tumor classification in MRI image using convolutional neural network,” Math. Biosci. Eng., vol. 17, no. 5, pp. 6203–6216, 2020, doi: 10.3934/mbe.2020328.
[23] S. Hasan, G. Rabbi, R. Islam, H. Imam Bijoy, and A. Hakim, “Bangla Font Recognition using Transfer Learning Method,” in 2022 International Conference on Inventive Computation Technologies (ICICT), Jul. 2022, pp. 57–62, doi: 10.1109/ICICT54344.2022.9850765.
[24] V. Rajinikanth, A. N. Joseph Raj, K. P. Thanaraj, and G. R. Naik, “A Customized VGG19 Network with Concatenation of Deep and Handcrafted Features for Brain Tumor Detection,” Appl. Sci., vol. 10, no. 10, p. 3429, May 2020, doi: 10.3390/app10103429.
[25] A. Pramanik, M. H. I. Bijoy, and M. S. Rahman, “Detection of Potholes using Convolutional Neural Network Models: A Transfer Learning Approach,” in 2021 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON), Dec. 2021, pp. 73–78, doi: 10.1109/RAAICON54709.2021.9929623.
[26] P. Harish and S. Baskar, “MRI based detection and classification of brain tumor using enhanced faster R-CNN and Alex Net model,” Mater. Today Proc., Dec. 2020, doi: 10.1016/j.matpr.2020.11.495.
[27] D. Hirahara, “Preliminary assessment for the development of CADe system for brain tumor in MRI images utilizing transfer learning in Xception model,” in 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), Oct. 2019, pp. 922–924, doi: 10.1109/GCCE46687.2019.9015529.
[28] N. Noreen, S. Palaniappan, A. Qayyum, I. Ahmad, M. Imran, and M. Shoaib, “A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor,” in IEEE Access, 2020, vol. 8, pp. 55135–55144, doi: 10.1109/ACCESS.2020.2978629.
[29] M. P. Mahmud, M. A. Ali, S. Akter, and M. H. I. Bijoy, “Lychee Tree Disease Classification and Prediction using Transfer Learning,” in 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Oct. 2022, pp. 1–7, doi: 10.1109/ICCCNT54827.2022.9984286.
[30] M. J. Mia, S. K. Maria, S. S. Taki, and A. A. Biswas, “Cucumber disease recognition using machine learning and transfer learning,” Bull. Electr. Eng. Informatics, vol. 10, no. 6, pp. 3432–3443, Dec. 2021, doi: 10.11591/eei.v10i6.3096.
[31] M. M. Fouad, E. M. Mostafa, and M. A. Elshafey, “Detection and localization enhancement for satellite images with small forgeries using modified GAN-based CNN structure,” Int. J. Adv. Intell. Informatics, vol. 6, no. 3, p. 278, Nov. 2020, doi: 10.26555/ijain.v6i3.548.
[32] M. R. Mia, M. J. Mia, A. Majumder, S. Supriya, and M. T. Habib, “Computer Vision Based Local Fruit Recognition,” Int. J. Eng. Adv. Technol., vol. 9, no. 1, pp. 2810–2820, Oct. 2019, doi: 10.35940/ijeat.A9789.109119.
[33] J. S. Murugaiyan, M. Palaniappan, T. Durairaj, and V. Muthukumar, “Fish species recognition using transfer learning techniques,” Int. J. Adv. Intell. Informatics, vol. 7, no. 2, p. 188, Jul. 2021, doi: 10.26555/ijain.v7i2.610.
[34] J. Cheng et al., “Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition,” PLoS One, vol. 10, no. 10, p. e0140381, Oct. 2015, doi: 10.1371/journal.pone.0140381.
[35] M. R. Ismael and I. Abdel-Qader, “Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network,” in 2018 IEEE International Conference on Electro/Information Technology (EIT), May 2018, vol. 2018-May, pp. 0252–0257, doi: 10.1109/EIT.2018.8500308.
[36] A. Pashaei, H. Sajedi, and N. Jazayeri, “Brain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines,” in 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE), Oct. 2018, pp. 314–319, doi: 10.1109/ICCKE.2018.8566571.
[37] N. Abiwinanda, M. Hanif, S. T. Hesaputra, A. Handayani, and T. R. Mengko, “Brain Tumor Classification Using Convolutional Neural Network,” in IFMBE Proceedings, vol. 68, no. 1, Springer Verlag, 2019, pp. 183–189, doi: 10.1007/978-981-10-9035-6_33.
[38] P. Afshar, K. N. Plataniotis, and A. Mohammadi, “Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries,” in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019, vol. 2019-May, pp. 1368–1372, doi: 10.1109/ICASSP.2019.8683759.
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