Detection and Classification of Brain tumors using Deep Learning and Compressive Sensing in magnetic resonance imaging
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Compressive sensing, Deep learning, magnetic resonance imaging, image classification, convolutional neural network, brain tumors
In this article he presents a new methodology for the detection of brain tumors in magnetic resonance imaging. The FIGSHARE database of the University of Southern Medicine, Guangzhou, China, was used. Mathematical morphology techniques were used for image conditioning to detect the area of interest in the image. Additionally, an algorithm was developed to determine the type of slice (axial, sagittal or coronal). For this procedure, the statistical mathematical technique k-means was used. Likewise, a pattern extraction was performed from each image using compressive sensing (CS). For the detection, segmentation and classification of the tumors, Deep learning based on convolutional neuronal networks was implemented applying R-CNN (regions with convolutional networks). The results obtained in the validation stage achieved a classification and detection of brain tumors in magnetic resonance images with high percentages of accuracy of 87.5% and 95.2%. The data was divided into 65% for network training, 18% for the test and 17% for the validation process. Finally, by applying deep learning and compressive sensing, brain tumors were detected and classified into three different types: meningioma, glioma, and pituitary with an accuracy rate of 87.5% and 95.2%
D. Sridhar and I. Murali Krishna, "Brain Tumor Classification using Discrete Cosine Transform and Probabilistic Neural Network," 2013 International Conference on Signal Processing, Image Processing & Pattern Recognition, Coimbatore, 2013, pp. 92-96.
A. Sehgal, S. Goel, P. Mangipudi, A. Mehra and D. Tyagi, "Automatic brain tumor segmentation and extraction in MR images," 2016 Conference on Advances in Signal Processing (CASP), Pune, 2016, pp. 104-107
R. Liu, L.O Hall, D.B. Goldgof, M. Zhou, R.A Gatenby and K.B. Ahmed, "Exploring deep features from brain tumor magnetic resonance images via transfer learning," 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, 2016, pp. 235-242
S. Kumar, C. Dabas, and S. Godara, “Classification of Brain MRI Tumor Images: A Hybrid Approach,” Procedia Comput. Sci., vol. 122, pp. 510–517, 2017
S. Lu, Z. Lu, X. Hou, H. Cheng and S. Wang, "Detection of cerebral microbleeding based on deep convolutional neural network," 2017, 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, 2017, pp. 93-96
M. H. O. Rashid, M. A. Mamun, M. A. Hossain and M. P. Uddin, "Brain Tumor Detection Using Anisotropic Filtering, SVM Classifier and Morphological Operation from MR Images," 2018, International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi, 2018, pp. 1-4
H. Mohsen, E.-S. A. El-Dahshan, E.-S. M. El-Horbaty, and A.-B. M. Salem, “Classification using deep learning neural networks for brain tumors,” Procedia Comput. Sci., vol. 3, no. 1, pp. 68–71, 2017. doi: 10.1016/j.fcij.2017.12.001
S. M. Kamrul Hasan and C. A. Linte, "A Modified U-Net Convolutional Network Featuring a Nearest-neighbor Re-sampling-based Elastic-Transformation for Brain Tissue Characterization and Segmentation," 2018, IEEE Western New York Image and Signal Processing Workshop (WNYISPW), Rochester, NY, 2018, pp. 1-5.
S. T. Kebir and S. Mekaoui, "An Efficient Methodology of Brain Abnormalities Detection using CNN Deep Learning Network," 2018, International Conference on Applied Smart Systems (ICASS), Medea, Algeria, 2018, pp. 1-5.
P. A. S. Deepak, "Brain tumor classification using deep CNN features via transfer learning", Computers in Biology and Medicine, vol. 111, 2019
P. Kumar Mallick, S. H. Ryu, S. K. Satapathy, S. Mishra, G. N. Nguyen and P. Tiwari, "Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network," in IEEE Access, vol. 7, pp. 46278-46287, 2019
P. Liu, L. Zhao and Y. Ma, "Compressive sensing of multispectral image based on PCA and Bregman split," 2013, IEEE International Geoscience and Remote Sensing Symposium - IGARSS, Melbourne, VIC, 2013, pp. 2558-2561
C. J. Della Porta, A. A. Bekit, B. H. Lampe and C. Chang, "Hyperspectral Image Classification via Compressive Sensing," in IEEE Transactions on Geoscience and Remote Sensing
J. Cheng, «figshare. Dataset., » brain tumor dataset, 2 4 2017. [En línea]. Available: https://doi.org/10.6084/m9.figshare.1512427.v5. [Último acceso: 10 07 2019]
P. Arora, Deepali, and S. Varshney, “Analysis of K-Means and K-Medoids Algorithm for Big Data,” Procedia Comput. Sci., vol. 78, no.1, pp. 507–512, 2016
L. E. Mendoza. L. M. Meriño, "Compresión robusta usando compressive sensing (CS)", Revista Colombiana de Tegnologías de Avanzada, vol. 1, nº 33, 2019
A. S. Lundervold and A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” Z. Med. Phys., vol. 29, no. 2, pp. 102–127, 2019
S. Zhang, L. Yao, A. Sun, and Y. Tay, "Deep learning based recommender system: A survey and new perspectives", ACM Computing Surveys (CSUR), vol. 52, no. 1, pp. 1-38, 2019
S. Minaee, N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu and J. Gao, "Deep learning--based text classification: a comprehensive review", ACM Computing Surveys (CSUR), vol. 54, no. 3, pp. 1-40, 2021
H. Ismail Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P. A. Muller, "Deep learning for time series classification: a review", Data mining and knowledge discovery, 33(4), 917-963, 2019
P. Bharati and A. Pramanik, "Deep learning techniques—R-CNN to mask R-CNN: a survey", In Computational Intelligence in Pattern Recognition (pp. 657-668), 2020
S. Li, W. Song, L. Fang, Y. Chen, P. Ghamisi and J. A. Benediktsson, "Deep learning for hyperspectral image classification: An overview". IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 6690-6709, 2019
A. Mikołajczyk and M. Grochowski, "Data augmentation for improving deep learning in image classification problem", In 2018 international interdisciplinary PhD workshop (IIPhDW) (pp. 117-122). IEEE
X. Yang, Y. Ye, X. Li, X., R. Y. Lau, X. Zhang, and X. Huang, "Hyperspectral image classification with deep learning models". IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 9, pp. 5408-5423, 2018
O. Stephen, M. Sain, U. J. Maduh, and D. U. Jeong, "An efficient deep learning approach to pneumonia classification in healthcare", Journal of healthcare engineering, 2019
M. Z. Hossain, F. Sohel, M. F. Shiratuddin and H. A. Laga, "Ccomprehensive survey of deep learning for image captioning", ACM Computing Surveys (CsUR), vol. 51, no. 6, pp. 1-36. 2019
V. Antun, F. Renna, C. Poon, B. Adcock and A. C. Hansen, "On instabilities of deep learning in image reconstruction and the potential costs of AI", Proceedings of the National Academy of Sciences, vol. 117, no. 48, pp. 30088-30095, 2020
L. Jiao and J. Zhao, "A survey on the new generation of deep learning in image processing", IEEE Access, 7, 172231-172263, 2019
C. Tian, Y. Xu, L. Fei, and K. Yan, "Deep learning for image denoising: A survey". In International Conference on Genetic and Evolutionary Computing (pp. 563-572). Springer, Singapore, 2018
Z. Guo, X. Li, H. Huang, N. Guo, and Q. Li, "Deep learning-based image segmentation on multimodal medical imaging". IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 3, no. 2, pp. 162-169, 2019
J. Dong, S. Roth and B. Schiele, "Deep wiener deconvolution: Wiener meets deep learning for image deblurring". Advances in Neural Information Processing Systems, vol. 33, pp. 1048-1059, 2020
C. Shorten, T. M. Khoshgoftaar and B. Furht, "Deep Learning applications for COVID-19", Journal of big Data, vol. 8, no. 1, pp. 1-54, 2021
S. Kuutti, R. Bowden, Y. Jin, P. Barber and S. Fallah,." A survey of deep learning applications to autonomous vehicle control", IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 2, pp. 712-733, 2020
O. Avci, O. Abdeljaber, S. Kiranyaz, M. Hussein, M. Gabbouj and D. J. Inman, D. J. "A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications", Mechanical systems and signal processing, vol. 147, pp. 107077, 2021
O. A. Montesinos-López, A. Montesinos-López, P. Pérez-Rodríguez, J. A. Barrón-López, J. W. Martini, S. B. Fajardo-Flores and J. & Crossa, "A review of deep learning applications for genomic selection", BMC genomics, vol. 22, no. 1, pp. 1-23, 2021
A. Esteva, K. Chou, S. Yeung, N. Naik, A. Madani, A. Mottaghi and R. Socher, "Deep learning-enabled medical computer vision", NPJ digital medicine, vol. 4, no. 1, 1-9, 2021
Y. Fu, Y. Lei, T. Wang, W. J. Curran, T. Liu and X. Yang, "Deep learning in medical image registration: a review", Physics in Medicine & Biology, vol. 65, no. 20, 20TR01, 2020
M. H. Hesamian, W. Jia, X. He and P. Kennedy, "Deep learning techniques for medical image segmentation: achievements and challenges", Journal of digital imaging, vol. 32, no. 4, 582-596, 2019
J. Zhang, Y. Xie, Q. Wu and Y. Xia, "Medical image classification using synergic deep learning", Medical image analysis, vol. 54, pp. 10-19, 2019
S. K. Zhou, H. Greenspan, C. Davatzikos, J. S. Duncan, B. Van Ginneken, A. Madabhushi and R. M. Summers, "A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises", Proceedings of the IEEE, vol. 109, no. 5, pp. 820-838, 2021
G. Currie, K. E. Hawk, E. Rohren, A. Vial and R. Klein, "Machine learning and deep learning in medical imaging: intelligent imaging", Journal of medical imaging and radiation sciences, vol. 50, no. 4, 477-487, 2019
In this article he presents a new methodology for the detection of brain tumors in magnetic resonance imaging. The FIGSHARE database of the University of Southern Medicine, Guangzhou, China, was used. Mathematical morphology techniques were used for image conditioning to detect the area of interest in the image. Additionally, an algorithm was developed to determine the type of slice (axial, sagittal or coronal). For this procedure, the statistical mathematical technique k-means was used. Likewise, a pattern extraction was performed from each image using compressive sensing (CS). For the detection, segmentation and classification of the tumors, Deep learning based on convolutional neuronal networks was implemented applying R-CNN (regions with convolutional networks). The results obtained in the validation stage achieved a classification and detection of brain tumors in magnetic resonance images with high percentages of accuracy of 87.5% and 95.2%. The data was divided into 65% for network training, 18% for the test and 17% for the validation process. Finally, by applying deep learning and compressive sensing, brain tumors were detected and classified into three different types: meningioma, glioma, and pituitary with an accuracy rate of 87.5% and 95.2%
D. Sridhar and I. Murali Krishna, "Brain Tumor Classification using Discrete Cosine Transform and Probabilistic Neural Network," 2013 International Conference on Signal Processing, Image Processing & Pattern Recognition, Coimbatore, 2013, pp. 92-96.
A. Sehgal, S. Goel, P. Mangipudi, A. Mehra and D. Tyagi, "Automatic brain tumor segmentation and extraction in MR images," 2016 Conference on Advances in Signal Processing (CASP), Pune, 2016, pp. 104-107
R. Liu, L.O Hall, D.B. Goldgof, M. Zhou, R.A Gatenby and K.B. Ahmed, "Exploring deep features from brain tumor magnetic resonance images via transfer learning," 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, 2016, pp. 235-242
S. Kumar, C. Dabas, and S. Godara, “Classification of Brain MRI Tumor Images: A Hybrid Approach,” Procedia Comput. Sci., vol. 122, pp. 510–517, 2017
S. Lu, Z. Lu, X. Hou, H. Cheng and S. Wang, "Detection of cerebral microbleeding based on deep convolutional neural network," 2017, 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, 2017, pp. 93-96
M. H. O. Rashid, M. A. Mamun, M. A. Hossain and M. P. Uddin, "Brain Tumor Detection Using Anisotropic Filtering, SVM Classifier and Morphological Operation from MR Images," 2018, International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi, 2018, pp. 1-4
H. Mohsen, E.-S. A. El-Dahshan, E.-S. M. El-Horbaty, and A.-B. M. Salem, “Classification using deep learning neural networks for brain tumors,” Procedia Comput. Sci., vol. 3, no. 1, pp. 68–71, 2017. doi: 10.1016/j.fcij.2017.12.001
S. M. Kamrul Hasan and C. A. Linte, "A Modified U-Net Convolutional Network Featuring a Nearest-neighbor Re-sampling-based Elastic-Transformation for Brain Tissue Characterization and Segmentation," 2018, IEEE Western New York Image and Signal Processing Workshop (WNYISPW), Rochester, NY, 2018, pp. 1-5.
S. T. Kebir and S. Mekaoui, "An Efficient Methodology of Brain Abnormalities Detection using CNN Deep Learning Network," 2018, International Conference on Applied Smart Systems (ICASS), Medea, Algeria, 2018, pp. 1-5.
P. A. S. Deepak, "Brain tumor classification using deep CNN features via transfer learning", Computers in Biology and Medicine, vol. 111, 2019
P. Kumar Mallick, S. H. Ryu, S. K. Satapathy, S. Mishra, G. N. Nguyen and P. Tiwari, "Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network," in IEEE Access, vol. 7, pp. 46278-46287, 2019
P. Liu, L. Zhao and Y. Ma, "Compressive sensing of multispectral image based on PCA and Bregman split," 2013, IEEE International Geoscience and Remote Sensing Symposium - IGARSS, Melbourne, VIC, 2013, pp. 2558-2561
C. J. Della Porta, A. A. Bekit, B. H. Lampe and C. Chang, "Hyperspectral Image Classification via Compressive Sensing," in IEEE Transactions on Geoscience and Remote Sensing
J. Cheng, «figshare. Dataset., » brain tumor dataset, 2 4 2017. [En línea]. Available: https://doi.org/10.6084/m9.figshare.1512427.v5. [Último acceso: 10 07 2019]
P. Arora, Deepali, and S. Varshney, “Analysis of K-Means and K-Medoids Algorithm for Big Data,” Procedia Comput. Sci., vol. 78, no.1, pp. 507–512, 2016
L. E. Mendoza. L. M. Meriño, "Compresión robusta usando compressive sensing (CS)", Revista Colombiana de Tegnologías de Avanzada, vol. 1, nº 33, 2019
A. S. Lundervold and A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” Z. Med. Phys., vol. 29, no. 2, pp. 102–127, 2019
S. Zhang, L. Yao, A. Sun, and Y. Tay, "Deep learning based recommender system: A survey and new perspectives", ACM Computing Surveys (CSUR), vol. 52, no. 1, pp. 1-38, 2019
S. Minaee, N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu and J. Gao, "Deep learning--based text classification: a comprehensive review", ACM Computing Surveys (CSUR), vol. 54, no. 3, pp. 1-40, 2021
H. Ismail Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P. A. Muller, "Deep learning for time series classification: a review", Data mining and knowledge discovery, 33(4), 917-963, 2019
P. Bharati and A. Pramanik, "Deep learning techniques—R-CNN to mask R-CNN: a survey", In Computational Intelligence in Pattern Recognition (pp. 657-668), 2020
S. Li, W. Song, L. Fang, Y. Chen, P. Ghamisi and J. A. Benediktsson, "Deep learning for hyperspectral image classification: An overview". IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 6690-6709, 2019
A. Mikołajczyk and M. Grochowski, "Data augmentation for improving deep learning in image classification problem", In 2018 international interdisciplinary PhD workshop (IIPhDW) (pp. 117-122). IEEE
X. Yang, Y. Ye, X. Li, X., R. Y. Lau, X. Zhang, and X. Huang, "Hyperspectral image classification with deep learning models". IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 9, pp. 5408-5423, 2018
O. Stephen, M. Sain, U. J. Maduh, and D. U. Jeong, "An efficient deep learning approach to pneumonia classification in healthcare", Journal of healthcare engineering, 2019
M. Z. Hossain, F. Sohel, M. F. Shiratuddin and H. A. Laga, "Ccomprehensive survey of deep learning for image captioning", ACM Computing Surveys (CsUR), vol. 51, no. 6, pp. 1-36. 2019
V. Antun, F. Renna, C. Poon, B. Adcock and A. C. Hansen, "On instabilities of deep learning in image reconstruction and the potential costs of AI", Proceedings of the National Academy of Sciences, vol. 117, no. 48, pp. 30088-30095, 2020
L. Jiao and J. Zhao, "A survey on the new generation of deep learning in image processing", IEEE Access, 7, 172231-172263, 2019
C. Tian, Y. Xu, L. Fei, and K. Yan, "Deep learning for image denoising: A survey". In International Conference on Genetic and Evolutionary Computing (pp. 563-572). Springer, Singapore, 2018
Z. Guo, X. Li, H. Huang, N. Guo, and Q. Li, "Deep learning-based image segmentation on multimodal medical imaging". IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 3, no. 2, pp. 162-169, 2019
J. Dong, S. Roth and B. Schiele, "Deep wiener deconvolution: Wiener meets deep learning for image deblurring". Advances in Neural Information Processing Systems, vol. 33, pp. 1048-1059, 2020
C. Shorten, T. M. Khoshgoftaar and B. Furht, "Deep Learning applications for COVID-19", Journal of big Data, vol. 8, no. 1, pp. 1-54, 2021
S. Kuutti, R. Bowden, Y. Jin, P. Barber and S. Fallah,." A survey of deep learning applications to autonomous vehicle control", IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 2, pp. 712-733, 2020
O. Avci, O. Abdeljaber, S. Kiranyaz, M. Hussein, M. Gabbouj and D. J. Inman, D. J. "A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications", Mechanical systems and signal processing, vol. 147, pp. 107077, 2021
O. A. Montesinos-López, A. Montesinos-López, P. Pérez-Rodríguez, J. A. Barrón-López, J. W. Martini, S. B. Fajardo-Flores and J. & Crossa, "A review of deep learning applications for genomic selection", BMC genomics, vol. 22, no. 1, pp. 1-23, 2021
A. Esteva, K. Chou, S. Yeung, N. Naik, A. Madani, A. Mottaghi and R. Socher, "Deep learning-enabled medical computer vision", NPJ digital medicine, vol. 4, no. 1, 1-9, 2021
Y. Fu, Y. Lei, T. Wang, W. J. Curran, T. Liu and X. Yang, "Deep learning in medical image registration: a review", Physics in Medicine & Biology, vol. 65, no. 20, 20TR01, 2020
M. H. Hesamian, W. Jia, X. He and P. Kennedy, "Deep learning techniques for medical image segmentation: achievements and challenges", Journal of digital imaging, vol. 32, no. 4, 582-596, 2019
J. Zhang, Y. Xie, Q. Wu and Y. Xia, "Medical image classification using synergic deep learning", Medical image analysis, vol. 54, pp. 10-19, 2019
S. K. Zhou, H. Greenspan, C. Davatzikos, J. S. Duncan, B. Van Ginneken, A. Madabhushi and R. M. Summers, "A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises", Proceedings of the IEEE, vol. 109, no. 5, pp. 820-838, 2021
G. Currie, K. E. Hawk, E. Rohren, A. Vial and R. Klein, "Machine learning and deep learning in medical imaging: intelligent imaging", Journal of medical imaging and radiation sciences, vol. 50, no. 4, 477-487, 2019
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