MRI-Based Brain Tumor Classification using ResNet-50 and Optimized Softmax Regression
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Abstract
Accurate classification of brain tumors is crucial for effective treatment planning and patient management. This study presents a new hybrid deep learning classification method based on transfer learning by feature extraction to automate the categorization of MRI brain image datasets into four classes: meningioma, glioma, pituitary tumor, and no tumor. The proposed method combines a finely-tuned ResNet-50 model, a state-of-the-art convolutional neural network architecture, with optimized Softmax Regression (SR) for classification. The study explores the use of data augmentation techniques and evaluates the model's performance on both augmented and unaugmented images. The results demonstrate that the proposed method achieves an impressive accuracy of 98.4%, outperforming existing methods for automatic brain tumor detection. Furthermore, a detailed comparative analysis is presented to evaluate the proposed model's accuracy and efficiency against previous state-of-the-art hybrid models for brain tumor classification. The study suggests that the proposed methodology could be employed as a diagnostic tool to aid radiologists in identifying questionable brain regions, potentially improving the accuracy and efficiency of brain tumor diagnosis.
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[1]
M. Musa, “MRI-Based Brain Tumor Classification using ResNet-50 and Optimized Softmax Regression”, INFOTEL, vol. 16, no. 3, pp. 598–614, Sep. 2024.
Section
Informatics
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References
References
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[7] A. Sawner, S. Shukla, V. Roy, S. Shukla, and S. Kapoor, "4 Way Classification of Brain Tumor Using Shallow Convolution Neural Network," EasyChair 2516-2314, 2021.
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[9] W. Yafooz, A. Alsaeedi, R. Alluhaibi, and M. E. Abdel-Hamid, "Enhancing multi-class web video categorization model using machine and deep learning approaches," Int. J. Electr. Comput. Eng, vol. 12, p. 3176, 2022.
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[20] S. A. Yazdan, R. Ahmad, N. Iqbal, A. Rizwan, A. N. Khan, and D.-H. Kim, "An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD," Tomography, vol. 8, pp. 1905-1927, 2022.
[21] M. Turuk, R. Sreemathy, S. Kadiyala, S. Kotecha, and V. Kulkarni, "CNN Based Deep Learning Approach for Automatic Malaria Parasite Detection," IAENG International Journal of Computer Science, vol. 49, 2022.
[22] D. A. Forsyth, J. L. Mundy, V. di Gesú, R. Cipolla, Y. LeCun, P. Haffner, et al., "Object recognition with gradient-based learning," Shape, contour and grouping in computer vision, pp. 319-345, 1999.
[23] S. Wang, D. Liu, Z. Yang, C. Feng, and R. Yao, "A convolutional neural network image classification based on extreme learning machine," IAENG Int. J. Comput. Sci, vol. 48, pp. 1-5, 2021.
[24] S. A. Agnes, J. Anitha, S. I. A. Pandian, and J. D. Peter, "Classification of mammogram images using multiscale all convolutional neural network (MA-CNN)," Journal of medical systems, vol. 44, pp. 1-9, 2020.
[25] T. Rajesh and R. S. M. Malar, "Rough set theory and feed forward neural network based brain tumor detection in magnetic resonance images," in International Conference on Advanced Nanomaterials & Emerging Engineering Technologies, 2013, pp. 240-244.
[26] A. Krol and B. Gimi, "Medical imaging 2017: biomedical applications in molecular, structural, and functional imaging," in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 2017.
[27] I. Wahlang, A. K. Maji, G. Saha, P. Chakrabarti, M. Jasinski, Z. Leonowicz, et al., "Brain magnetic resonance imaging classification using deep learning architectures with gender and age," Sensors, vol. 22, p. 1766, 2022.
[28] A. Biswas and M. S. Islam, "Brain tumor types classification using K-means clustering and ANN approach," in 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), 2021, pp. 654-658.
[29] Z. Luo, Z. Zhu, and B. Zhang, "An SCADTV nonconvex regularization approach for magnetic resonance imaging," IAENG International Journal of Computer Science, vol. 48, pp. 1005-1012, 2021.
[30] A. Rehman, S. Naz, M. I. Razzak, F. Akram, and M. Imran, "A deep learning-based framework for automatic brain tumors classification using transfer learning," Circuits, Systems, and Signal Processing, vol. 39, pp. 757-775, 2020.
[31] E. Irmak, "Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework," Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 45, pp. 1015-1036, 2021.
[32] S. Deepak and P. Ameer, "Automated categorization of brain tumor from mri using cnn features and svm," Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 8357-8369, 2021.
[33] S. Bhuvaji, A. Kadam, P. Bhumkar, S. Dedge, and S. Kanchan, "Brain tumor classification (MRI)," Kaggle, vol. 10, 2020.
[34] M. Musa, S. Sengupta, and Y. Chen, "Mri-compatible soft robotic sensing pad for head motion detection," IEEE Robotics and Automation Letters, vol. 7, pp. 3632-3639, 2022.
[35] E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, "Autoaugment: Learning augmentation strategies from data," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 113-123.
[36] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, pp. 84-90, 2017.
[37] S. J. Pan and Q. Yang, "A survey on transfer learning IEEE transactions on knowledge and data engineering," 22 (10), pp. 1345-1359, 2009.
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[39] H. Sharma and A. S. Jalal, "An improved attention and hybrid optimization technique for visual question answering," Neural Processing Letters, pp. 1-22, 2022.
[1] S. Abbasi and F. Tajeripour, "Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient," Neurocomputing, vol. 219, pp. 526-535, 2017.
[2] M. K. Abd-Ellah, A. I. Awad, A. A. Khalaf, and H. F. Hamed, "Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks," EURASIP Journal on Image and Video Processing, vol. 2018, pp. 1-10, 2018.
[3] D. N. Louis, A. Perry, G. Reifenberger, A. Von Deimling, D. Figarella-Branger, W. K. Cavenee, et al., "The 2016 World Health Organization classification of tumors of the central nervous system: a summary," Acta neuropathologica, vol. 131, pp. 803-820, 2016.
[4] M. M. Badža and M. Č. Barjaktarović, "Classification of brain tumors from MRI images using a convolutional neural network," Applied Sciences, vol. 10, p. 1999, 2020.
[5] N. Alalwan, A. Abozeid, A. A. ElHabshy, and A. Alzahrani, "Efficient 3D deep learning model for medical image semantic segmentation," Alexandria Engineering Journal, vol. 60, pp. 1231-1239, 2021.
[6] P. M. de Azevedo-Marques and J. R. Ferreira, "Medical image analyst: A radiology career focused on comprehensive quantitative imaging analytics to improve healthcare," Academic Radiology, vol. 29, p. 170, 2022.
[7] A. Sawner, S. Shukla, V. Roy, S. Shukla, and S. Kapoor, "4 Way Classification of Brain Tumor Using Shallow Convolution Neural Network," EasyChair 2516-2314, 2021.
[8] Q. T. Ostrom, H. Gittleman, P. M. De Blank, J. L. Finlay, J. G. Gurney, R. McKean-Cowdin, et al., "American brain tumor association adolescent and young adult primary brain and central nervous system tumors diagnosed in the United States in 2008-2012," Neuro-oncology, vol. 18, pp. i1-i50, 2016.
[9] W. Yafooz, A. Alsaeedi, R. Alluhaibi, and M. E. Abdel-Hamid, "Enhancing multi-class web video categorization model using machine and deep learning approaches," Int. J. Electr. Comput. Eng, vol. 12, p. 3176, 2022.
[10] S. Ben Atitallah, M. Driss, W. Boulila, A. Koubaa, and H. Ben Ghezala, "Fusion of convolutional neural networks based on Dempster–Shafer theory for automatic pneumonia detection from chest X‐ray images," International Journal of Imaging Systems and Technology, vol. 32, pp. 658-672, 2022.
[11] M. Rasool, N. A. Ismail, W. Boulila, A. Ammar, H. Samma, W. M. Yafooz, et al., "A Hybrid Deep Learning Model for Brain Tumour Classification," Entropy, vol. 24, p. 799, 2022.
[12] J. Kang, Z. Ullah, and J. Gwak, "MRI-based brain tumor classification using ensemble of deep features and machine learning classifiers," Sensors, vol. 21, p. 2222, 2021.
[13] M. Maalouf, T. B. Trafalis, and I. Adrianto, "Kernel logistic regression using truncated Newton method," Computational management science, vol. 8, p. 415, 2011.
[14] Y. Jiao, J. Yuan, Y. Qiang, and S. Fei, "Deep embeddings and logistic regression for rapid active learning in histopathological images," Computer Methods and Programs in Biomedicine, vol. 212, p. 106464, 2021.
[15] C.-M. L. Eduardo, V.-P. N. Enrique, and M.-D. R. Javier, "Heterogeneous Processing for Estimating the Parameters of a Binary Logistic Regression Model Using the Fisher Scoring Algorithm," 2021.
[16] H. Selvaraj, S. T. Selvi, D. Selvathi, and L. Gewali, "Brain MRI slices classification using least squares support vector machine," International journal of intelligent computing in medical sciences & image processing, vol. 1, pp. 21-33, 2007.
[17] P. John, "Brain tumor classification using wavelet and texture based neural network," International Journal of Scientific & Engineering Research, vol. 3, pp. 1-7, 2012.
[18] Z. Ullah, M. U. Farooq, S.-H. Lee, and D. An, "A hybrid image enhancement based brain MRI images classification technique," Medical hypotheses, vol. 143, p. 109922, 2020.
[19] M. Sharif, U. Tanvir, E. U. Munir, M. A. Khan, and M. Yasmin, "Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection," Journal of ambient intelligence and humanized computing, pp. 1-20, 2018.
[20] S. A. Yazdan, R. Ahmad, N. Iqbal, A. Rizwan, A. N. Khan, and D.-H. Kim, "An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD," Tomography, vol. 8, pp. 1905-1927, 2022.
[21] M. Turuk, R. Sreemathy, S. Kadiyala, S. Kotecha, and V. Kulkarni, "CNN Based Deep Learning Approach for Automatic Malaria Parasite Detection," IAENG International Journal of Computer Science, vol. 49, 2022.
[22] D. A. Forsyth, J. L. Mundy, V. di Gesú, R. Cipolla, Y. LeCun, P. Haffner, et al., "Object recognition with gradient-based learning," Shape, contour and grouping in computer vision, pp. 319-345, 1999.
[23] S. Wang, D. Liu, Z. Yang, C. Feng, and R. Yao, "A convolutional neural network image classification based on extreme learning machine," IAENG Int. J. Comput. Sci, vol. 48, pp. 1-5, 2021.
[24] S. A. Agnes, J. Anitha, S. I. A. Pandian, and J. D. Peter, "Classification of mammogram images using multiscale all convolutional neural network (MA-CNN)," Journal of medical systems, vol. 44, pp. 1-9, 2020.
[25] T. Rajesh and R. S. M. Malar, "Rough set theory and feed forward neural network based brain tumor detection in magnetic resonance images," in International Conference on Advanced Nanomaterials & Emerging Engineering Technologies, 2013, pp. 240-244.
[26] A. Krol and B. Gimi, "Medical imaging 2017: biomedical applications in molecular, structural, and functional imaging," in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 2017.
[27] I. Wahlang, A. K. Maji, G. Saha, P. Chakrabarti, M. Jasinski, Z. Leonowicz, et al., "Brain magnetic resonance imaging classification using deep learning architectures with gender and age," Sensors, vol. 22, p. 1766, 2022.
[28] A. Biswas and M. S. Islam, "Brain tumor types classification using K-means clustering and ANN approach," in 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), 2021, pp. 654-658.
[29] Z. Luo, Z. Zhu, and B. Zhang, "An SCADTV nonconvex regularization approach for magnetic resonance imaging," IAENG International Journal of Computer Science, vol. 48, pp. 1005-1012, 2021.
[30] A. Rehman, S. Naz, M. I. Razzak, F. Akram, and M. Imran, "A deep learning-based framework for automatic brain tumors classification using transfer learning," Circuits, Systems, and Signal Processing, vol. 39, pp. 757-775, 2020.
[31] E. Irmak, "Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework," Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 45, pp. 1015-1036, 2021.
[32] S. Deepak and P. Ameer, "Automated categorization of brain tumor from mri using cnn features and svm," Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 8357-8369, 2021.
[33] S. Bhuvaji, A. Kadam, P. Bhumkar, S. Dedge, and S. Kanchan, "Brain tumor classification (MRI)," Kaggle, vol. 10, 2020.
[34] M. Musa, S. Sengupta, and Y. Chen, "Mri-compatible soft robotic sensing pad for head motion detection," IEEE Robotics and Automation Letters, vol. 7, pp. 3632-3639, 2022.
[35] E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, "Autoaugment: Learning augmentation strategies from data," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 113-123.
[36] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, pp. 84-90, 2017.
[37] S. J. Pan and Q. Yang, "A survey on transfer learning IEEE transactions on knowledge and data engineering," 22 (10), pp. 1345-1359, 2009.
[38] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
[39] H. Sharma and A. S. Jalal, "An improved attention and hybrid optimization technique for visual question answering," Neural Processing Letters, pp. 1-22, 2022.