Deep Neural Network Approach Based Segmentation, Detection and Classification of Brain Tumor
Journal of Engineering Research and Reports,
Page 41-50
DOI:
10.9734/jerr/2022/v22i917563
Abstract
The segmentation, detection and extraction of malignant tumor regions from magnetic resonance (MR) images are challenging tasks in medical image analysis. Approaches based on machine and deep learning have been introduced, which performed better than traditional image processing methods. However, many approaches still show limited ability due to the complex dataset and image modalities. This study evaluated the deep learning approach's performance and traditional image processing algorithms for medical imaging segmentation, detection, and classification. The proposed system comprises multiple stages. The Median filters are used in the pre-processing step, and morphological operation and Otsu thresholding are used to segment MR images. Discrete Wavelet Transform (DWT) algorithm is considered in the extraction features, and their classification is executed by a convolutional neural network (CNN) and support vector machine (SVM) algorithms. The Mat lab has been used for simulation and experimental findings to evaluate the suggested method's performance on the brain's complex and highly 2D structures. The results show that the methodology is reliable and efficient, with 93.5% accuracy.
Keywords:
- Image processing
- brain tumor detection
- image segmentation
- MRI
- SVM
- deep learning
- multilayer neural network
How to Cite
Zaman, A., Yu, L., Din, N. U., Ullah, K., & Hayat, Q. (2022). Deep Neural Network Approach Based Segmentation, Detection and Classification of Brain Tumor. Journal of Engineering Research and Reports, 22(9), 41-50. https://doi.org/10.9734/jerr/2022/v22i917563
References
Despotović I, Goossens B, Philips W. “MRI segmentation of the human brain: Challenges, methods, and applications,”Computational and Mathematical Methods in Medicine, vol. 2015. Hindawi Publishing Corporation; 2015.
Ben naceur M, Saouli R, Akil M, Kachouri R. Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images,”Comput. Methods Programs Biomed. 2018;166:39–49.
EI. Zacharaki et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn. Reson. Med. 2009;62(6):1609–1618.
Devi N, Bhattacharyya K. Automatic Brain Tumor Detection and Classification of Grades of Astrocytoma. In Lecture Notes in Networks and Systems. Springer, 2018;24:125–135.
El-Dahshan EAS, H. M. Mohsen, K. Revett, and A. B. M. Salem, “Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm,”Expert Syst. Appl. 2014; 41(11):5526–5545.
Havaei M, et al. Brain tumor segmentation with Deep Neural Networks,”Med. Image Anal. 2017; 35:18–31.
Shinde AS, Desai VV, Chavan MN. Challenges inherent in building an intelligent paradigm for tumor detection using machine learning algorithms. In Advances in Intelligent Systems and Computing, 2018;705:153–161.
Lai H, Pan Y, Liu Y, Yan S. Simultaneous feature learning and hash coding with deep neural networks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015;3270–3278.
Samanta AK, Khan AA. Computer Aided Diagnostic System for Automatic Detection of Brain Tumor Through MRI Using Clustering Based Segmentation Technique and SVM Classifier,” in Advances in Intelligent Systems and Computing, 2018;723, pp. 343–351.
Damodharan S, Raghavan D. Combining tissue segmentation and neural network for brain tumor detection. Int. Arab J. Inf. Technol. 2015;12(1):42–52.
Alfonse M, Salem A-BM. An Automatic Classification of Brain Tumors through MRI Using Support Vector Machine; 2016.
Harley AW, Derpanis KG, Kokkinos I. Segmentation-Aware Convolutional Networks Using Local Attention Masks. In Proceedings of the IEEE International Conference on Computer Vision. 2017; 5048–5057.
Liu D, Ma L, Chen H, Meng K. Tumor disease diagnosis model based on BP neural network,” in Proceedings - 2017 International Conference on Smart Grid and Electrical Automation, ICSGEA 2017. 2017;308–311.
Pinheiro PO, Collobert R. From image-level to pixel-level labeling with Convolutional Networks. in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2015;1713–1721.
Lee CS, Tyring AJ, Deruyter NP, Wu Y, Rokem A, Lee AY. Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed. Opt. Express. 2017;8(7):3440.
Bao L, Wu B, and W. Liu, CNN in MRF: Video Object Segmentation via Inference in a CNN-Based Higher-Order Spatio-Temporal MRF. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2018;5977–5986.
Dong H, Yang G, Liu F, Mo Y, Guo Y. Automatic brain tumor detection and segmentation using U-net based fully convolutional networks. In Communications in Computer and Information Science. 2017;723:506–517.
Oliva D, Abd Elaziz M, Hinojosa S. Image Processing. In Studies in Computational Intelligence, Springer Verlag. 2019;825: 27–45.
He HJ, Zheng C, Sun DW. Image Segmentation Techniques. In Computer Vision Technology for Food Quality Evaluation: Second Edition, Elsevier Inc. 2016;45–63.
Ivanovici M, Richard N, Paulus D. Color image segmentation,” in Advanced Color Image Processing and Analysis, vol. 9781441961, Springer New York, 2013;219–277.
Chen D. Image segmentation. in User Centered Design for Medical Visualization, IGI Global, 2008;258–279.
Ulku EE, Camurcu AY. Computer aided brain tumor detection with histogram equalization and morphological image processing techniques. in 2013 International Conference on Electronics, Computer and Computation, ICECCO. 2013;48–51.
Bhargavi K, Jyothi S. A Survey on Threshold Based Segmentation Technique in Image Processing. Int. J. Innov. Res. Dev. 2014;3(12):234–39.
Krstinić D, Skelin AK, Slapničar I. Fast two-step histogram-based image segmentation,”IET Image Process. 2011;5(1):63–72.
Pereira S, Pinto A, Alves V, Silva CA. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. IEEE Trans. Med. Imaging. 2016;35(5):1240–1251.
Kumar G, Bhatia PK. A detailed review of feature extraction in image processing systems. In International Conference on Advanced Computing and Communication Technologies, ACCT, 2014;5–12.
Arizmendi C, Vellido A, Romero E. Classification of human brain tumours from MRS data using Discrete Wavelet Transform and Bayesian Neural Networks. Expert Syst. Appl. 2012;39(5): 5223–5232.
Ghasemzadeh A, Demirel H. 3D discrete wavelet transform-based feature extraction for hyperspectral face recognition. IET Biometric. 2018;7(1):49–55.
Corso JJ, Sharon E, Dube S, El-Saden S, Sinha U, Yuille A. Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE Trans. Med. Imaging. 2008;27(5):629–640.
Ayalapogu RR, Pabboju S, Ramisetty RR. Analysis of dual tree M-band wavelet transform based features for brain image classification. Magn. Reson. Med. 2018; 80(6):2393–2401.
Abd-Ellah MK, Awad AI, Khalaf AAM, Hamed HFA. Classification of brain tumor MRIs using a kernel support vector machine. in Communications in Computer and Information Science, 2016;636:151–160.
Nandpuru HB, Salankar SS, Bora VR. MRI brain cancer classification using support vector machine,” in 2014 IEEE Students’ Conference on Electrical, Electronics and Computer Science, SCEECS; 2014.
Bauer SLP. Nolte, Reyes M. Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011;6893 LNCS, no. PART 3:354–361.
Krizhevsky A, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM. 2017;60(6):84–90.
SAZLI MH. A brief review of feed-forward neural networks,” Commun. Fac. Sci. Univ. Ankara, 2006;11–17.
Cui S, Mao L, Jiang J, Liu C, Xiong S. Automatic semantic segmentation of brain gliomas from MRI images using a deep cascaded neural network. J. Healthc. Eng. 2018;2018.
Pattanayak S. Pro Deep Learning with TensorFlow A Mathematical Approach to Advanced Artificial Intelligence in Python; 2018.
Vala M, Baxi A. A review on Otsu image segmentation algorithm,”Int. J. Adv. Res. Comput. Eng. Technol. 2013;2(2):387-389.
The BraTS datasets can be found at the below link address.
Available:https://www.kaggle.com/datasets/awsaf49/brats2020-training-data
Ben naceur M, Saouli R, Akil M, Kachouri R. Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images,”Comput. Methods Programs Biomed. 2018;166:39–49.
EI. Zacharaki et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn. Reson. Med. 2009;62(6):1609–1618.
Devi N, Bhattacharyya K. Automatic Brain Tumor Detection and Classification of Grades of Astrocytoma. In Lecture Notes in Networks and Systems. Springer, 2018;24:125–135.
El-Dahshan EAS, H. M. Mohsen, K. Revett, and A. B. M. Salem, “Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm,”Expert Syst. Appl. 2014; 41(11):5526–5545.
Havaei M, et al. Brain tumor segmentation with Deep Neural Networks,”Med. Image Anal. 2017; 35:18–31.
Shinde AS, Desai VV, Chavan MN. Challenges inherent in building an intelligent paradigm for tumor detection using machine learning algorithms. In Advances in Intelligent Systems and Computing, 2018;705:153–161.
Lai H, Pan Y, Liu Y, Yan S. Simultaneous feature learning and hash coding with deep neural networks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015;3270–3278.
Samanta AK, Khan AA. Computer Aided Diagnostic System for Automatic Detection of Brain Tumor Through MRI Using Clustering Based Segmentation Technique and SVM Classifier,” in Advances in Intelligent Systems and Computing, 2018;723, pp. 343–351.
Damodharan S, Raghavan D. Combining tissue segmentation and neural network for brain tumor detection. Int. Arab J. Inf. Technol. 2015;12(1):42–52.
Alfonse M, Salem A-BM. An Automatic Classification of Brain Tumors through MRI Using Support Vector Machine; 2016.
Harley AW, Derpanis KG, Kokkinos I. Segmentation-Aware Convolutional Networks Using Local Attention Masks. In Proceedings of the IEEE International Conference on Computer Vision. 2017; 5048–5057.
Liu D, Ma L, Chen H, Meng K. Tumor disease diagnosis model based on BP neural network,” in Proceedings - 2017 International Conference on Smart Grid and Electrical Automation, ICSGEA 2017. 2017;308–311.
Pinheiro PO, Collobert R. From image-level to pixel-level labeling with Convolutional Networks. in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2015;1713–1721.
Lee CS, Tyring AJ, Deruyter NP, Wu Y, Rokem A, Lee AY. Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed. Opt. Express. 2017;8(7):3440.
Bao L, Wu B, and W. Liu, CNN in MRF: Video Object Segmentation via Inference in a CNN-Based Higher-Order Spatio-Temporal MRF. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2018;5977–5986.
Dong H, Yang G, Liu F, Mo Y, Guo Y. Automatic brain tumor detection and segmentation using U-net based fully convolutional networks. In Communications in Computer and Information Science. 2017;723:506–517.
Oliva D, Abd Elaziz M, Hinojosa S. Image Processing. In Studies in Computational Intelligence, Springer Verlag. 2019;825: 27–45.
He HJ, Zheng C, Sun DW. Image Segmentation Techniques. In Computer Vision Technology for Food Quality Evaluation: Second Edition, Elsevier Inc. 2016;45–63.
Ivanovici M, Richard N, Paulus D. Color image segmentation,” in Advanced Color Image Processing and Analysis, vol. 9781441961, Springer New York, 2013;219–277.
Chen D. Image segmentation. in User Centered Design for Medical Visualization, IGI Global, 2008;258–279.
Ulku EE, Camurcu AY. Computer aided brain tumor detection with histogram equalization and morphological image processing techniques. in 2013 International Conference on Electronics, Computer and Computation, ICECCO. 2013;48–51.
Bhargavi K, Jyothi S. A Survey on Threshold Based Segmentation Technique in Image Processing. Int. J. Innov. Res. Dev. 2014;3(12):234–39.
Krstinić D, Skelin AK, Slapničar I. Fast two-step histogram-based image segmentation,”IET Image Process. 2011;5(1):63–72.
Pereira S, Pinto A, Alves V, Silva CA. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. IEEE Trans. Med. Imaging. 2016;35(5):1240–1251.
Kumar G, Bhatia PK. A detailed review of feature extraction in image processing systems. In International Conference on Advanced Computing and Communication Technologies, ACCT, 2014;5–12.
Arizmendi C, Vellido A, Romero E. Classification of human brain tumours from MRS data using Discrete Wavelet Transform and Bayesian Neural Networks. Expert Syst. Appl. 2012;39(5): 5223–5232.
Ghasemzadeh A, Demirel H. 3D discrete wavelet transform-based feature extraction for hyperspectral face recognition. IET Biometric. 2018;7(1):49–55.
Corso JJ, Sharon E, Dube S, El-Saden S, Sinha U, Yuille A. Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE Trans. Med. Imaging. 2008;27(5):629–640.
Ayalapogu RR, Pabboju S, Ramisetty RR. Analysis of dual tree M-band wavelet transform based features for brain image classification. Magn. Reson. Med. 2018; 80(6):2393–2401.
Abd-Ellah MK, Awad AI, Khalaf AAM, Hamed HFA. Classification of brain tumor MRIs using a kernel support vector machine. in Communications in Computer and Information Science, 2016;636:151–160.
Nandpuru HB, Salankar SS, Bora VR. MRI brain cancer classification using support vector machine,” in 2014 IEEE Students’ Conference on Electrical, Electronics and Computer Science, SCEECS; 2014.
Bauer SLP. Nolte, Reyes M. Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011;6893 LNCS, no. PART 3:354–361.
Krizhevsky A, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM. 2017;60(6):84–90.
SAZLI MH. A brief review of feed-forward neural networks,” Commun. Fac. Sci. Univ. Ankara, 2006;11–17.
Cui S, Mao L, Jiang J, Liu C, Xiong S. Automatic semantic segmentation of brain gliomas from MRI images using a deep cascaded neural network. J. Healthc. Eng. 2018;2018.
Pattanayak S. Pro Deep Learning with TensorFlow A Mathematical Approach to Advanced Artificial Intelligence in Python; 2018.
Vala M, Baxi A. A review on Otsu image segmentation algorithm,”Int. J. Adv. Res. Comput. Eng. Technol. 2013;2(2):387-389.
The BraTS datasets can be found at the below link address.
Available:https://www.kaggle.com/datasets/awsaf49/brats2020-training-data
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