Automatic Brain Tumor Detection in Brain MRI Images using Deep Learning Methods | ||
| Journal of AI and Data Mining | ||
| دوره 12، شماره 1، فروردین 2024، صفحه 27-35 اصل مقاله (1.42 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2024.13148.2452 | ||
| نویسندگان | ||
| Farima Fakouri1؛ Mohsen Nikpour* 1؛ Abbas Soleymani Amiri2 | ||
| 1Department of Computer and Biomedical Engineerig, Mazandaran Institute of Technology, Babol, Iran. | ||
| 2Department of Medical Science, Babol University of Medical Science, Babol, Iran. | ||
| چکیده | ||
| Due to the increased mortality caused by brain tumors, accurate and fast diagnosis of brain tumors is necessary to implement the treatment of this disease. In this research, brain tumor classification performed using a network based on ResNet architecture in MRI images. MRI images that available in the cancer image archive database included 159 patients. First, two filters called median and Gaussian filters were used to improve the quality of the images. An edge detection operator is also used to identify the edges of the image. Second, the proposed network was first trained with the original images of the database, then with Gaussian filtered and Median filtered images. Finally, accuracy, specificity and sensitivity criteria have been used to evaluate the results. Proposed method in this study was lead to 87.21%, 90.35% and 93.86% accuracy for original, Gaussian filtered and Median filtered images. Also, the sensitivity and specificity was calculated 82.3% and 84.3% for the original images, respectively. Sensitivity for Gaussian and Median filtered images was calculated 90.8% and 91.57%, respectively and specificity was calculated 93.01% and 93.36%, respectively. As a conclusion, image processing approaches in preprocessing stage should be investigated to improve the performance of deep learning networks. | ||
| کلیدواژهها | ||
| Deep Learning؛ Image Processing؛ Automatic Detection؛ Brain Tumor؛ MRI | ||
| مراجع | ||
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[1] A. Isla, F. Alvarez, A. Gonzalez, A. García-Grande, M. Perez-Alvarez, and M. García-Blazquez, "Brain tumor and pregnancy," Obstetrics & Gynecology, vol. 89, no. 1, pp. 19-23, 1997.
[2] A. Neuville, F. Chibon, and J.-M. Coindre, "Grading of soft tissue sarcomas: from histological to molecular assessment," Pathology, vol. 46, no. 2, pp. 113-120, 2014.
[3] A. R. Loughan et al., "Death-related distress in adult primary brain tumor patients," Neuro-Oncology Practice, vol. 7, no. 5, pp. 498-506, 2020.
[4] D. T. Blumenthal and L. A. Cannon-Albright, "Familiality in brain tumors," Neurology, vol. 71, no. 13, pp. 1015-1020, 2008.
[5] S. Kumar, C. Dabas, and S. Godara, "Classification of brain MRI tumor images: a hybrid approach," Procedia computer science, vol. 122, pp. 510-517, 2017.
[6] G. Mohan and M. M. Subashini, "MRI based medical image analysis: Survey on brain tumor grade classification," Biomedical Signal Processing and Control, vol. 39, pp. 139-161, 2018.
[7] Saiful Bukhori, Muhammad Almas Bariiqy, Windi Eka Y. R, Januar Adi Putra , “Segmentation of Breast Cancer using Convolutional Neural Network and U-Net Architecture”, Journal of Artificial Intelligence and Data Mining (JAIDM), Vol. 11, No. 3, pp. 477-485, 2023.[8] Y. Gu et al., "Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs," Computers in biology and medicine, vol. 103, pp. 220-231, 2018.
[9] A. O’Shea, G. Lightbody, G. Boylan, and A. Temko, "Investigating the impact of CNN depth on neonatal seizure detection performance," in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018, pp. 5862-5865: IEEE.
[10] M. Mobini and G. Kaddoum, "Deep chaos synchronization," IEEE Open Journal of the Communications Society, vol. 1, pp. 1571-1582, 2020.
[11] M. A. Schwemmer et al., "Meeting brain–computer interface user performance expectations using a deep neural network decoding framework," Nature medicine, vol. 24, no. 11, pp. 1669-1676, 2018.
[12] H. Zuo, H. Fan, E. Blasch, and H. Ling, "Combining convolutional and recurrent neural networks for human skin detection," IEEE Signal Processing Letters, vol. 24, no. 3, pp. 289-293, 2017.
[13] O. Charron, A. Lallement, D. Jarnet, V. Noblet, J.-B. Clavier, and P. Meyer, "Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network," Computers in biology and medicine, vol. 95, pp. 43-54, 2018.
[14] L. Shao, F. Zhu, and X. Li, "Transfer learning for visual categorization: A survey," IEEE transactions on neural networks and learning systems, vol. 26, no. 5, pp. 1019-1034, 2014.
[15] L. Zhou, Z. Zhang, Y.-C. Chen, Z.-Y. Zhao, X.-D. Yin, and H.-B. Jiang, "A deep learning-based radiomics model for differentiating benign and malignant renal tumors," Translational oncology, vol. 12, no. 2, pp. 292-300, 2019.
[16] E. Deniz, A. Şengür, Z. Kadiroğlu, Y. Guo, V. Bajaj, and Ü. Budak, "Transfer learning based histopathologic image classification for breast cancer detection," Health information science and systems, vol. 6, pp. 1-7, 2018.
[17] S. Hussein, P. Kandel, C. W. Bolan, M. B. Wallace, and U. Bagci, "Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches," IEEE transactions on medical imaging, vol. 38, no. 8, pp. 1777-1787, 2019.
[18] J. Cheng et al., "Enhanced performance of brain tumor classification via tumor region augmentation and partition," PloS one, vol. 10, no. 10, p. e0140381, 2015.
[19] Y. Yang et al., "Glioma grading on conventional MR images: a deep learning study with transfer learning," Frontiers in neuroscience, vol. 12, p. 804, 2018.
[20] Z. N. K. Swati et al., "Content-based brain tumor retrieval for MR images using transfer learning," IEEE Access, vol. 7, pp. 17809-17822, 2019.
[21] J. Cheng et al., "Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation," PloS one, vol. 11, no. 6, p. e0157112, 2016.
[22] S. Lu, S.-H. Wang, and Y.-D. Zhang, "Detecting pathological brain via ResNet and randomized neural networks," Heliyon, vol. 6, no. 12, p. e05625, 2020.
[23] D. Liu, Y. Liu, and L. Dong, "G-ResNet: Improved ResNet for Brain Tumor Classification," 2019, pp. 535-545.
[24] M. Gholizadeh-Ansari, J. Alirezaie, and P. Babyn, "Deep learning for low-dose CT denoising using perceptual loss and edge detection layer," Journal of digital imaging, vol. 33, pp. 504-515, 2020.
[25] D.-X. Xue, R. Zhang, H. Feng, and Y.-L. Wang, "CNN-SVM for microvascular morphological type recognition with data augmentation," Journal of medical and biological engineering, vol. 36, pp. 755-764, 2016.
[26] M. Hilts and C. Duzenli, "Image filtering for improved dose resolution in CT polymer gel dosimetry," Medical physics, vol. 31, no. 1, pp. 39-49, 2004.
[27] M. H. Chowdhury and W. D. Little, "Image thresholding techniques," in IEEE pacific Rim conference on communications, computers, and signal processing. Proceedings, 1995, pp. 585-589: IEEE.
[28] S. Perumal and T. Velmurugan, "Preprocessing by contrast enhancement techniques for medical images," International Journal of Pure and Applied Mathematics, vol. 118, no. 18, pp. 3681-3688, 2018.
[29] M. R. Ogiela and R. Tadeusiewicz, "Preprocessing medical images and their overall enhancement," Modern Computational Intelligence Methods for the Interpretation of Medical Images, pp. 65-97, 2008.
[30] I. T. Young and L. J. Van Vliet, "Recursive implementation of the Gaussian filter," Signal processing, vol. 44, no. 2, pp. 139-151, 1995.
[31] A. Kumar and S. S. Sodhi, "Comparative analysis of gaussian filter, median filter and denoise autoenocoder," in 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom), 2020, pp. 45-51: IEEE.
[32] P. Bao, L. Zhang, and X. Wu, "Canny edge detection enhancement by scale multiplication," IEEE transactions on pattern analysis and machine intelligence, vol. 27, no. 9, pp. 1485-1490, 2005.
[33] 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.
[34] A. Tulasi, S. Malarselvi, and M. D. Pappu, "A Review on MobileNet, ResNet and SqueezeNet for iOS & iPadOS for on Device Training and Prediction using CoreML," 2021.
[35] R. Singh and B. B. Agarwal, "An automated brain tumor classification in MR images using an enhanced convolutional neural network," International Journal of Information Technology, vol. 15, no. 2, pp. 665-674, 2023.
[36] P. Saxena, A. Maheshwari, and S. Maheshwari, "Predictive modeling of brain tumor: a deep learning approach," in Innovations in Computational Intelligence and Computer Vision: Proceedings of ICICV 2020: Springer, 2020, pp. 275-285.
[37] A. Gumaei, M. M. Hassan, M. R. Hassan, A. Alelaiwi, and G. Fortino, "A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification," IEEE Access, vol. 7, pp. 36266-36273, 2019. | ||
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