Detección y clasificación de tumores cerebrales mediante aprendizaje profundo y detección compresiva en imágenes de resonancia magnética
Publicado
:Número
:Sección
:Licencia
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
Cómo citar
Sensación compresiva, aprendizaje profundo, imágenes de resonancia magnética, clasificación de imágenes, red neuronal convolucional, tumores cerebrales
En este artículo presenta una nueva metodología para la detección de tumores cerebrales en imágenes de resonancia magnética. Se utilizó la base de datos FIGSHARE de la Universidad de Medicina del Sur, Guangzhou, China. Se utilizaron técnicas de morfología matemática para el acondicionamiento de la imagen con el fin de detectar el área de interés en la imagen. Además, se desarrolló un algoritmo para determinar el tipo de corte (axial, sagital o coronal). Para este procedimiento se utilizó la técnica matemática estadística k-means. Asimismo, se realizó una extracción de patrones a partir de cada imagen utilizando la técnica de detección compresiva (CS). Para la detección, segmentación y clasificación de los tumores se implementó el aprendizaje profundo basado en redes neuronales convolucionales aplicando R-CNN (regiones con redes convolucionales). Los resultados obtenidos en la etapa de validación lograron una clasificación y detección de tumores cerebrales en imágenes de resonancia magnética con altos porcentajes de precisión del 87,5% y 95,2%. Los datos se dividieron en un 65% para el entrenamiento de la red, un 18% para la prueba y un 17% para el proceso de validación. Finalmente, aplicando deep learning y compressive sensing, se detectaron y clasificaron los tumores cerebrales en tres tipos diferentes: meningioma, glioma y pituitaria con un porcentaje de precisión del 87,5% y 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
En este artículo presenta una nueva metodología para la detección de tumores cerebrales en imágenes de resonancia magnética. Se utilizó la base de datos FIGSHARE de la Universidad de Medicina del Sur, Guangzhou, China. Se utilizaron técnicas de morfología matemática para el acondicionamiento de la imagen con el fin de detectar el área de interés en la imagen. Además, se desarrolló un algoritmo para determinar el tipo de corte (axial, sagital o coronal). Para este procedimiento se utilizó la técnica matemática estadística k-means. Asimismo, se realizó una extracción de patrones a partir de cada imagen utilizando la técnica de detección compresiva (CS). Para la detección, segmentación y clasificación de los tumores se implementó el aprendizaje profundo basado en redes neuronales convolucionales aplicando R-CNN (regiones con redes convolucionales). Los resultados obtenidos en la etapa de validación lograron una clasificación y detección de tumores cerebrales en imágenes de resonancia magnética con altos porcentajes de precisión del 87,5% y 95,2%. Los datos se dividieron en un 65% para el entrenamiento de la red, un 18% para la prueba y un 17% para el proceso de validación. Finalmente, aplicando deep learning y compressive sensing, se detectaron y clasificaron los tumores cerebrales en tres tipos diferentes: meningioma, glioma y pituitaria con un porcentaje de precisión del 87,5% y 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
Artículos más leídos del mismo autor/a
- , , ADQUISICIÓN Y PROCESAMIENTO DE SEÑALES EMG PARA CONTROLAR MOVIMIENTO DE UN BRAZO HIDRAULICO , Mundo FESC: Vol. 4 Núm. 7 (2014)
- Sandra Milena Castro-Escobar, Leonor Jaimes-Cerveleón, Zoraima Peñaranda-Ayala, Zulmary Nieto-Sánchez, Seis sigma para la solución de problemas de la calidad. Caso de estudio proceso de envasado de café molido , Mundo FESC: Vol. 11 Núm. s4 (2021)
- Carlos Arturo Parra-Ortega, Leonor Jaimes Cerveleón, Luis Enrique Mendoza, Optimización Mediante Programación Dinámica de un Sistema de Producción: Caso Aluminio , Mundo FESC: Vol. 13 Núm. S1 (2023)
- Leonor Jaimes-Cerveleón, Javier Dario Fernández-Ledesma, Luis Enrique Mendoza, Herramientas de optimización para el sector carbón del departamento Norte de Santander , Mundo FESC: Vol. 10 Núm. 19 (2020)
- Fredy Alonso Fernández Gelvez , Leonor Jaimes Cerveleón, Luís Enrique Mendoza, Modelo de regresión basado en máquinas de aprendizaje utilizando datos estadísticos del café Colombiano , Mundo FESC: Vol. 13 Núm. S1 (2023)
- Jessica Ferley Domínguez-Rangel, Luis Enrique Mendoza, Carlos Alberto Pacheco-Sánchez, Modelo financiero para el control de la productividad del capital de trabajo en las empresas del sector calzado en Cúcuta, Norte de Santander , Mundo FESC: Vol. 11 Núm. S6 (2021)
- Jessica Ferley Dominguez Rangel, Luis Enrique Mendoza, Zulmary Carolina Nieto Sánchez, Gemelos digitales energéticamente conscientes en manufactura sostenible. Revisión bibliométrica , Mundo FESC: Vol. 17 Núm. 37 (2027)