Accurate Brain Tumor Image Segmentation and Classification Using Deep Convolutional Network (DCNN) and Pulse Coupled Neural Networks (PCNN)
,
Abstract
Purpose: Nowadays, detecting brain tumors is a crucial application. If a tumor is discovered later on, the medical issues are significant. Therefore, early diagnosis is essential. Magnetic Resonance Imaging (MRI) is the most recent detection, diagnosis, and assessment technology.
Materials and Methods: In this study, MRI images are segmented before input to a pulse-coupled neural network model to identify the existence of a tumor in the brain picture. The doctor may turn to this model for assistance if there are more input MRI brain pictures. This work preprocesses the images using normalization smoothing with linear filter and adaptive histogram. Statistical and Local Binary Patterns (LBP) features are extracted from the preprocessed images to perform the classification process. The Deep Convolutional Network (DCNN) is used to segment the image. The Pulse Coupled Neural Networks (PCNN) categorize the input images as normal and tumor.
Results: Accuracy, sensitivity, specificity, and precision are the various metrics evaluated. This work achieves 99.35 accuracies, 99.78 sensitivity, 98.45 specificities, and 97.61 precision. This work is compared with previous implementations to measure performance.
Conclusion: The comparison analysis improves tumor segmentation and classification accuracy. The suggested method yields great outcomes.
1- Raza, Asaf, Huma Ayub, Javed Ali Khan, Ijaz Ahmad, Ahmed S. Salama, Yousef Ibrahim Daradkeh, Danish Javeed, Ateeq Ur Rehman, and Habib Hamam. "A hybrid deep learning-based approach for brain tumor classification." Electronics 11, no.7: 1146, (2022).
2- Woźniak M, Siłka J, Wieczorek M. Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Computing and Applications; 16:1-6, (2021).
3- Sobhaninia, Z., Rezaei, S., Karimi, N., Emami, A., & Samavi, S. Brain tumor segmentation by cascaded deep neural networks using multiple image scales. In 2020 28th Iranian Conference on Electrical Engineering (ICEE) (pp. 1-4). IEEE, (2020, August).
4- Kumar, Sharan, and Dattatreya P. Mankame. "Optimization driven deep convolution neural network for brain tumor classification." Biocybernetics and Biomedical Engineering 40, no. 3: 1190-1204, (2020).
5- Zhou, T., Canu, S., Vera, P., & Ruan, S. Brain tumor segmentation with missing modalities via latent multi-source correlation representation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 533-541). Springer, Cham, (2020, October).
6- Nayak, Dillip Ranjan, Neelamadhab Padhy, Pradeep Kumar Mallick, Mikhail Zymbler, and Sachin Kumar. "Brain tumor classification using dense efficient-net." Axioms 11, no. 1: 34, (2022).
7- Abd El Kader, Isselmou, Guizhi Xu, Zhang Shuai, Sani Saminu, Imran Javaid, and Isah Salim Ahmad. "Differential deep convolutional neural network model for brain tumor classification." Brain Sciences 11, no. 3: 352, (2021).
8-Ayadi, Wadhah, Imen Charfi, Wajdi Elhamzi, and Mohamed Atri. "Brain tumor classification based on hybrid approach." The Visual Computer 38, no. 1 (2022): 107-117.
9- Ullah, Z., Usman, M., Jeon, M., & Gwak, J. (2022). Cascade multiscale residual attention cnns with adaptive roi for automatic brain tumor segmentation. Information Sciences, 608, 1541-1556.
10- Sharif, M. I., Li, J. P., Khan, M. A., & Saleem, M. A. (2020). Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recognition Letters, 129, 181-189.
11- Amin, J., Sharif, M., Raza, M., Saba, T., & Anjum, M. A. (2019). Brain tumor detection using statistical and machine learning method. Computer Methods and Programs in Biomedicine, 177, 69-79.
12- Saba, T., Mohamed, A. S., El-Affendi, M., Amin, J., & Sharif, M. (2020). Brain tumor detection using fusion of hand crafted and deep learning features. Cognitive Systems Research, 59, 221-230.
13- Amin, J., Sharif, M., Yasmin, M., & Fernandes, S. L. (2020). A distinctive approach in brain tumor detection and classification using MRI. Pattern Recognition Letters, 139, 118-127.
14- Woźniak M, Siłka J, Wieczorek M. Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Computing and Applications. 2021; 16:1-6.
15- Toğaçar, M., Ergen, B., & Cömert, Z. (2020). BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Medical Hypotheses, 134, 109531.
16- Huang L, Ruan S, Denoeux T. 2021. Belief function-based semi-supervised learning for brain tumor segmentation. In2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) 2021 Apr 13 (pp. 160-164). IEEE.
17- Cheng G, Ji H. Adversarial perturbation on MRI modalities in brain tumor segmentation. IEEE Access. 2020 Oct 12; 8:206009-15.
18- Abd El Kader, I., Xu, G., Shuai, Z., Saminu, S., Javaid, I., & Salim Ahmad, I. (2021). Differential deep convolutional neural network model for brain tumor classification. Brain Sciences, 11(3), 352.
19- Rai, H. M., & Chatterjee, K. (2020). Detection of brain abnormality by a novel Lu-Net deep neural CNN model from MR images. Machine Learning with Applications, 2, 100004.
20- Siar, M., & Teshnehlab, M. (2019, October). Brain tumor detection using deep neural network and machine learning algorithm. In 2019 9th international conference on computer and knowledge engineering (ICCKE) (pp. 363-368). IEEE.
21- Subashini, M. M., & Sahoo, S. K. Brain tumor detection using Pulse coupled neural network (PCNN) and back propagation network, (2012).
22- Ullah Z, Usman M, Jeon M, Gwak J. Cascade multiscale residual attention cnns with adaptive roi for automatic brain tumor segmentation. Information Sciences; 1;608:1541-56, (2022)
23- Ramamoorthy, M., Qamar, S., Manikandan, R., Jhanjhi, N. Z., Masud, M., & AlZain, M. A. Earlier detection of brain tumor by preprocessing based on histogram equalization with neural network. In Healthcare (Vol. 10, No. 7, p. 1218). MDPI, (2022, June).
24- Kabir, M. A. Automatic brain tumor detection and feature extraction from mriimage. GSJ, 8(4), (2020).
25- Zeineldin RA, Karar ME, Elshaer Z, Wirtz CR, Burgert O, Mathis-Ullrich F. Explainability of deep neural networks for MRI analysis of brain tumors. International Journal of Computer Assisted Radiology and Surgery.; 23:1-1, (2022).
26- Kapoor, A., & Agarwal, R. Brain Image Segmentation based on U-Net Architecture with Adaptive Histogram Equalization. In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) (pp. 1-5). IEEE, (2021, September).
27- Amin, J., Sharif, M., Yasmin, M., & Fernandes, S. L. Big data analysis for brain tumor detection: Deep convolutional neural networks. Future Generation Computer Systems, 87, 290-297, (2018).
28- Deepak, S., & Ameer, P. M. Brain tumor classification using deep CNN features via transfer learning. Computers in Biology and Medicine, 111, 103345, (2019).
29- Irmak, E. 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, 45(3), 1015-1036, (2021).
30- Tang, Chaosheng, Bin Li, Junding Sun, Shui-Hua Wang, and Yu-Dong Zhang. "GAM-SpCaNet: gradient awareness minimization-based spinal convolution attention network for brain tumor classification." Journal of King Saud University-Computer and Information Sciences, (2023).
31- Ismael, M. R., & Abdel-Qader, I. Brain tumor classification via statistical features and back-propagation neural network. In 2018 IEEE international conference on electro/information technology (EIT) (pp. 0252-0257). IEEE, (2018, May).
32- Kaplan, K., Kaya, Y., Kuncan, M., & Ertunç, H. M. Brain tumor classification using modified local binary patterns (LBP) feature extraction methods. Medical Hypotheses, 139, 109696, (2020).
33- Henry T, Carré A, Lerousseau M, Estienne T, Robert C, Paragios N, Deutsch E. Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution. In International MICCAI Brainlesion Workshop 2020 Oct 4. Springer, Cham, (pp. 327-339), (2020).
34- Henry, Theophraste, Alexandre Carré, Marvin Lerousseau, Théo Estienne, Charlotte Robert, Nikos Paragios, and Eric Deutsch. "Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, Revised Selected Papers, Part I 6, pp. 327-339. Springer International Publishing, 2021, (2020).
35- Dehyadegari, L., & Khajehasani, S. Magnetic Resonance Imaging Image Segmentation and Brain Tumor Detection Using Pulse-Coupled Neural Networks. Journal of Engineering Science, 17(1), 1-16, (2021).
36- Niepceron, B., Grassia, F., & Moh, A. N. S. Brain Tumor Detection Using Selective Search and Pulse-Coupled Neural Network Feature Extraction. Computing and Informatics, 41(1), 253-270, (2022).
37- Shanker, R., & Bhattacharya, M. Classification of brain MR images using Modified version of Simplified Pulse-Coupled Neural Network and Linear Programming Twin Support Vector Machines. The Journal of Supercomputing, 1-33, (2022).
38- Li, M., Kuang, L., Xu, S., & Sha, Z. Brain tumor detection based on multimodal information fusion and convolutional neural network. IEEE Access, 7, 180134-180146, (2019).
39- Xiao, F., Lv, S., Zong, Z., Wu, L., Tang, X., Kuang, W., & Guo, H. Cerebrospinal fluid biomarkers for brain tumor detection: clinical roles and current progress. American Journal of Translational Research, 12(4), 1379, (2020).
2- Woźniak M, Siłka J, Wieczorek M. Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Computing and Applications; 16:1-6, (2021).
3- Sobhaninia, Z., Rezaei, S., Karimi, N., Emami, A., & Samavi, S. Brain tumor segmentation by cascaded deep neural networks using multiple image scales. In 2020 28th Iranian Conference on Electrical Engineering (ICEE) (pp. 1-4). IEEE, (2020, August).
4- Kumar, Sharan, and Dattatreya P. Mankame. "Optimization driven deep convolution neural network for brain tumor classification." Biocybernetics and Biomedical Engineering 40, no. 3: 1190-1204, (2020).
5- Zhou, T., Canu, S., Vera, P., & Ruan, S. Brain tumor segmentation with missing modalities via latent multi-source correlation representation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 533-541). Springer, Cham, (2020, October).
6- Nayak, Dillip Ranjan, Neelamadhab Padhy, Pradeep Kumar Mallick, Mikhail Zymbler, and Sachin Kumar. "Brain tumor classification using dense efficient-net." Axioms 11, no. 1: 34, (2022).
7- Abd El Kader, Isselmou, Guizhi Xu, Zhang Shuai, Sani Saminu, Imran Javaid, and Isah Salim Ahmad. "Differential deep convolutional neural network model for brain tumor classification." Brain Sciences 11, no. 3: 352, (2021).
8-Ayadi, Wadhah, Imen Charfi, Wajdi Elhamzi, and Mohamed Atri. "Brain tumor classification based on hybrid approach." The Visual Computer 38, no. 1 (2022): 107-117.
9- Ullah, Z., Usman, M., Jeon, M., & Gwak, J. (2022). Cascade multiscale residual attention cnns with adaptive roi for automatic brain tumor segmentation. Information Sciences, 608, 1541-1556.
10- Sharif, M. I., Li, J. P., Khan, M. A., & Saleem, M. A. (2020). Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recognition Letters, 129, 181-189.
11- Amin, J., Sharif, M., Raza, M., Saba, T., & Anjum, M. A. (2019). Brain tumor detection using statistical and machine learning method. Computer Methods and Programs in Biomedicine, 177, 69-79.
12- Saba, T., Mohamed, A. S., El-Affendi, M., Amin, J., & Sharif, M. (2020). Brain tumor detection using fusion of hand crafted and deep learning features. Cognitive Systems Research, 59, 221-230.
13- Amin, J., Sharif, M., Yasmin, M., & Fernandes, S. L. (2020). A distinctive approach in brain tumor detection and classification using MRI. Pattern Recognition Letters, 139, 118-127.
14- Woźniak M, Siłka J, Wieczorek M. Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Computing and Applications. 2021; 16:1-6.
15- Toğaçar, M., Ergen, B., & Cömert, Z. (2020). BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Medical Hypotheses, 134, 109531.
16- Huang L, Ruan S, Denoeux T. 2021. Belief function-based semi-supervised learning for brain tumor segmentation. In2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) 2021 Apr 13 (pp. 160-164). IEEE.
17- Cheng G, Ji H. Adversarial perturbation on MRI modalities in brain tumor segmentation. IEEE Access. 2020 Oct 12; 8:206009-15.
18- Abd El Kader, I., Xu, G., Shuai, Z., Saminu, S., Javaid, I., & Salim Ahmad, I. (2021). Differential deep convolutional neural network model for brain tumor classification. Brain Sciences, 11(3), 352.
19- Rai, H. M., & Chatterjee, K. (2020). Detection of brain abnormality by a novel Lu-Net deep neural CNN model from MR images. Machine Learning with Applications, 2, 100004.
20- Siar, M., & Teshnehlab, M. (2019, October). Brain tumor detection using deep neural network and machine learning algorithm. In 2019 9th international conference on computer and knowledge engineering (ICCKE) (pp. 363-368). IEEE.
21- Subashini, M. M., & Sahoo, S. K. Brain tumor detection using Pulse coupled neural network (PCNN) and back propagation network, (2012).
22- Ullah Z, Usman M, Jeon M, Gwak J. Cascade multiscale residual attention cnns with adaptive roi for automatic brain tumor segmentation. Information Sciences; 1;608:1541-56, (2022)
23- Ramamoorthy, M., Qamar, S., Manikandan, R., Jhanjhi, N. Z., Masud, M., & AlZain, M. A. Earlier detection of brain tumor by preprocessing based on histogram equalization with neural network. In Healthcare (Vol. 10, No. 7, p. 1218). MDPI, (2022, June).
24- Kabir, M. A. Automatic brain tumor detection and feature extraction from mriimage. GSJ, 8(4), (2020).
25- Zeineldin RA, Karar ME, Elshaer Z, Wirtz CR, Burgert O, Mathis-Ullrich F. Explainability of deep neural networks for MRI analysis of brain tumors. International Journal of Computer Assisted Radiology and Surgery.; 23:1-1, (2022).
26- Kapoor, A., & Agarwal, R. Brain Image Segmentation based on U-Net Architecture with Adaptive Histogram Equalization. In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) (pp. 1-5). IEEE, (2021, September).
27- Amin, J., Sharif, M., Yasmin, M., & Fernandes, S. L. Big data analysis for brain tumor detection: Deep convolutional neural networks. Future Generation Computer Systems, 87, 290-297, (2018).
28- Deepak, S., & Ameer, P. M. Brain tumor classification using deep CNN features via transfer learning. Computers in Biology and Medicine, 111, 103345, (2019).
29- Irmak, E. 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, 45(3), 1015-1036, (2021).
30- Tang, Chaosheng, Bin Li, Junding Sun, Shui-Hua Wang, and Yu-Dong Zhang. "GAM-SpCaNet: gradient awareness minimization-based spinal convolution attention network for brain tumor classification." Journal of King Saud University-Computer and Information Sciences, (2023).
31- Ismael, M. R., & Abdel-Qader, I. Brain tumor classification via statistical features and back-propagation neural network. In 2018 IEEE international conference on electro/information technology (EIT) (pp. 0252-0257). IEEE, (2018, May).
32- Kaplan, K., Kaya, Y., Kuncan, M., & Ertunç, H. M. Brain tumor classification using modified local binary patterns (LBP) feature extraction methods. Medical Hypotheses, 139, 109696, (2020).
33- Henry T, Carré A, Lerousseau M, Estienne T, Robert C, Paragios N, Deutsch E. Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution. In International MICCAI Brainlesion Workshop 2020 Oct 4. Springer, Cham, (pp. 327-339), (2020).
34- Henry, Theophraste, Alexandre Carré, Marvin Lerousseau, Théo Estienne, Charlotte Robert, Nikos Paragios, and Eric Deutsch. "Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, Revised Selected Papers, Part I 6, pp. 327-339. Springer International Publishing, 2021, (2020).
35- Dehyadegari, L., & Khajehasani, S. Magnetic Resonance Imaging Image Segmentation and Brain Tumor Detection Using Pulse-Coupled Neural Networks. Journal of Engineering Science, 17(1), 1-16, (2021).
36- Niepceron, B., Grassia, F., & Moh, A. N. S. Brain Tumor Detection Using Selective Search and Pulse-Coupled Neural Network Feature Extraction. Computing and Informatics, 41(1), 253-270, (2022).
37- Shanker, R., & Bhattacharya, M. Classification of brain MR images using Modified version of Simplified Pulse-Coupled Neural Network and Linear Programming Twin Support Vector Machines. The Journal of Supercomputing, 1-33, (2022).
38- Li, M., Kuang, L., Xu, S., & Sha, Z. Brain tumor detection based on multimodal information fusion and convolutional neural network. IEEE Access, 7, 180134-180146, (2019).
39- Xiao, F., Lv, S., Zong, Z., Wu, L., Tang, X., Kuang, W., & Guo, H. Cerebrospinal fluid biomarkers for brain tumor detection: clinical roles and current progress. American Journal of Translational Research, 12(4), 1379, (2020).
| Files | ||
| Issue | Articles in Press | |
| Section | Original Article(s) | |
| Keywords | ||
| Brain Tumor Adaptive Histogram Equalization Pulse Coupled Neural Networks Deep Convolutional Network Local Binary Patterns | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Borse MS, R MP, Babu TR. Accurate Brain Tumor Image Segmentation and Classification Using Deep Convolutional Network (DCNN) and Pulse Coupled Neural Networks (PCNN). Frontiers Biomed Technol. 2024;.
