Brain tumor detection from MRI images with using proposed deep learning model: the partial correlation-based channel selection

A brain tumor is an abnormal growth of a mass or cell in the brain. Early diagnosis of the tumor significantly increases the chances of successful treatment. Artificial intelligence-based systems can detect the tumor in early stages. In this way, it could be possible to detect a tumor and resolve this problem that may endanger human life early. In the study, the partial correlation-based channel selection formula was presented that allowed the selection of the most prominent feature that differs from the other studies in the literature. Additionally, the multi-channel convolution structure was proposed for the feature network phase of the Faster R-CNN architecture. In the proposed model, the most prominent features were obtained from the multi-channel selection structure in the feature network phase with the channel selection formula in the channel selection layer. The architecture was applied for the early detection of possible brain tumors, which are a severe risk for human life. Within the present study, the brain tumor was classified applying the proposed multi-channel Faster R-CNN based model with three different open-access datasets. VGG-16, faster region-based convolutional neural network (Faster R-CNN), DenseNet-201, Resnet-50, and SRN models, which are popular deep learning architectures, were applied to the same problem to compare the results and demonstrate the efficiency of the proposed model. Accuracy, sensitivity, and processing times of the applied methods were measured to demonstrate the models’ performance and efficiency. As a result, the highest accuracy rates were obtained using the proposed model as 98.31%, 99.6%, and 99.8% for three datasets. In addition, it was compared with related studies in the literature to demonstrate the proposed model’s applicability. The proposed model’s accuracy and performance proved to be higher than in the other studies.

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[1] Hossain J, Xiao W, Tayeb M, Khan S. Epidemiology and prognostic factors of pediatric brain tumor survival in the US: Evidence from four decades of population data. Cancer Epidemiology 2021; 72: 101942.
[2] McKinney PC. Brain tumours: Incidence, survival, and aetiology. Journal of Neurology Neurosurgery Psychiatry 2004; 75: 12-17.
[3] Thakkar P, Greenwald BD, Patel P. Rehabilitation of adult patients with primary brain tumors: a narrative review. Brain Sciences 2020; 10: 492. doi: 10.3390/brainsci10080492
[4] Parvataneni R, Polley M, Freeman T, Lamborn K, Prados M et al. Identifying the needs of brain tumor patients and their caregivers. Journal of Neuro-Onchology 2011; 104 (3): 737-744.
[5] Brain Tumor Dataset. Available online: https://figshare.com/articles/brain_tumor_dataset/1512427/5 (25.01.2021).
[6] Cheng J, Huang W, Shuangliang C, Yang R, Yang W et al. Enhanced performance of brain tumor classification via tumor region augmentation and partition. Plos Biology 2015; 10: 1-13. doi: 10.1371/ jour-nal.pone.0140381
[7] Kaggle Brain MRI Images for Brain Tumor Detection Dataset. Available online: https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection (25.01.2021).
[8] Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging 2015; 34 (10): 1993-2024. doi: 10.1109/TMI.2014.2377694
[9] Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M et al. The cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nature Scientific Data 2017; 4: 170117. doi: 10.1038/sdata.2017.117
[10] Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M et al. Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive 2017; 10: 1-13. doi: 10.7937/K9/TCIA.2017.KLXWJJ1Q
[11] Kakarla J, Isunuri, BV, Doppalapudi KS, Bylapudi KSR. Three-class classification of brain magnetic resonance images using average-pooling convolutional neural network. International Journal of Imaging Systems And Technology 2021; doi: 10.1002/ima.22554
[12] Saranya C, Priya G, Jayalakshmmi P, Pavithra EH. Brain tumor identification using deep learning. Materials Today: Proceedings 2021 doi: 10.1016/j.matpr.2020.11.555
[13] Karayegen G, Aksahin MF. Brain tumor prediction on MR images with semantic segmentation by using deep learning network and 3D imaging of tumor region. Biomedical Signal Processing and Control 2021; 66. doi: 10.1016/j.bspc.2021.102458
[14] Grovik E, Yi D, Iv M, Tong E, Rubin D, Zaharchuk G. Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI. Journal Of Magnetic Resonance Imaging 2020; 51 (1): 175-182. doi: 10.1002/jmri.26766
[15] Sharif MI, Li JP, Khan MA, Saleem MA. Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recognation Letters 2020; 129: 181-189. doi: 10.1016/j.patrec.2019.11.019
[16] Hollon TC, Pandian B, Adapa AR, Urias E, Save AV et al. Near real-time intraoperative brain tumor diagnosis using stimulated raman histology and deep neural networks. Nature Medicine 2020; 26 (1): 52. doi: 10.1038/s41591- 019-0715-9
[17] Rehman A, Naz S, Razzak MI, Akram F, Imran M. A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits Systems And Signal Processing 2020; 39 (2): 757-775.
[18] Khan HA, Jue W, Mushtaq M, Mushtaq MU. Brain tumor classification in MRI image using convolutional neural network. Mathematical Biosciences and Engineering 2020;17 (5):6203-16.
[19] Mehrotra R, Ansari MA, Agrawal R, Anand RS. A transfer learning approach for AI-based classification of brain tumors. Machine Learning with Applications 2020; 2:100003.
[20] Malathi M, Sinthia P. Brain tumour segmentation using convolutional neural network with tensor flow. Asian Pacific Journal of Cancer Prevention 2019; 20 (7): 2095–2101. doi: 10.31557/APJCP.2019.20.7.2095
[21] Sajjad M, Khan S, Muhammad K, Wu W, Ullah A et al. Multi-grade brain tumor classification using deep CNN with extensive data augmentation. Journal Of Computational Science 2019; 30: 174-82. doi: 10. 1016/j.jocs.2018.12.003
[22] Özyurt F, Sert E, Avcı E, Dogantekin E. Brain tumor detection based on convolutional neural network with neutrosophic expert maximum fuzzy sure entropy. Measurement 2019; 147. doi: 10.1016/ j.measurement.2019.07.058
[23] Alqudah AM, Hiam Alquraan H, Qasmieh IA, Alqudah A, Al-Sharu W. Brain tumor classification using deep learning technique - A comparison between cropped, uncropped, and segmented lesion images with different sizes. International Journal of Advanced Trends in Computer Science and Engineering 2019; 8 (6): 3684–3691. doi:10.30534/ijatcse/2019/155862019
[24] Widhiarso W, Yohannes Y, Prakarsah C. Brain tumor classification using gray level co-occurrence matrix and convolutional neural network. Indonesian Journal of Electronics and Instrumentation Systems 2018; 8: 179-190. doi: 10.22146/ijeis.34713
[25] Seetha J, Raja SS. Brain tumor classification using convolutional neural networks. Biomedical Pharmacology & Journal 2018; 11: 1457-1461. doi:10.1007/978-981-10-9035-6_33
[26] Wang G, Li W, Azuluaga M. Interactive medical image segmentation using deep learning with image specific fine tuning. IEEE Transactions on Medical Imaging 2018; 37: 1562–1573.
[27] 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. Journal of Healthcare Engineering 2018; 1: 1–14. doi: 10.1155/2018/4940593
[28] Khawaldeh S, Pervaiz U, Rafiq A, Alkhawaldeh RS. Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks. Applied Sciences 2017; 8 (1): 1-17. dpi: 10.3390/app8010027
[29] Havaei M, Davy A, Farley WD, Biard A, Courville A et al . Brain tumor segmentation with deep neural networks. Medical Image Analysis 2017; 35: 18-31.
[30] Hussain S, Anwar SM, Majidmil M. Brain tumor segmentation using cascaded deep convolutional neural network. In: IEEE 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Jeju Island, Korea; 2017. pp. 1998-2001.
[31] Dong H, Yang G, Liu F, Mo Y, Guo Y. Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. In: Valdez H (editor). Medical Image Understanding and Analysis. Edinburgh, UK: Springer International Publishing, 2017, pp. 506-517.
[32] Chinmayi P, Agilandeeswari L, Prabu KM, Muralibabu K. An efficient deep learning neural network based brain tumor detection system. International Journal of Pure and Applied Mathematics 2017; 117: 151-160.
[33] Işın A, Direkoğlu C, Şah M. Review Of MRI Review of MRI based brain tumor image segmentation using deep learning methods. Procedia Computer Science 2016; 102: 317 – 324.
[34] Pereira S, Pinto A, Alves V, Carlos AS. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Transactions on Medical Imaging 2016; 35: 1240-1251.
[35] Konstantinos KK, Ledig C, Virginia FJ, Simpson PJ, Kane AD et al. Efficient multi scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medica Image Analysis 2017; 36: 61-78.
[36] Xue F, Chunxiao C, Dongsheng L. Survival prediction of patients suffering from glioblastoma based on two-branch DenseNet using multi-channel features. International Journal of Computer Assisted Radiology and Surgery 2021; 16: 207-217.
[37] Deepak S, Ameer PM. Brain tumour classification using siamese neural network and neighbourhood analysis in embedded feature space. International Journal of Imaging Systems And Technology 2021; doi: 10.1002/ima.22543
[38] Ismael SA, Mohammed A, Hefny H. An enhanced deep learning approach for brain cancer MRI images classification using residual networks. Artificial Intelligence in Medicine 2020;102:101779.
[39] Nazir M, Khan MA, Saba T, Rehman A. Brain tumor detection from MRI images using multi-level wavelets. In: 2019 ICCIS International Conference on Computer and Information Sciences; Sakaka, Suudi Arabia; 2019. pp. 1-5. doi: 10.1109/ICCISci.2019.8716413
[40] Mohsen H, El-Sayed A, El-Dahshan, El-Sayed M, El-Horbaty et al. Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal 2018; 3: 68-71. doi: 10.1016/j.fcij.2017.12.001
[41] Sasikala M, Kumaravel N. A wavelet-based optimal texture feature set for classification of brain tumours. Journal Of Medical Engineering & Technology 2008; 32: 198-205. doi: 10.1080/03091900701455524
[42] Fan J, Ma C, Zhong Y. A selective overview of deep learning. Statistical Science 2021; 36 (2): 264-290. doi: 10.1214/20-STS783
[43] Çelik A, Arıca N. Enhancing face pose normalization with deep learning. Turkish Journal of Electrical Engineering & Computer Sciences 2019; 27: 3699-3712. doi: 10.3906/elk-1810-192
[44] Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 2015; 39 (6). doi:10.1109/TPAMI.2016.2577031
[45] Jiang Z, Shi X. Application research of key frames extraction technology combined with optimized faster R-CNN algorithm in traffic video analysis. Complexity 2021; 2021. doi:1 0.1155/2021/6620425
[46] Le QV, Ngiam J, Coates A, Lahiri A, Prochnow B et al. On optimization methods for deep learning. In: IEEE 28th International Conference on Machine Learning (ICML); Bellevue, WA, USA; 2011. pp. 265-272.
[47] Marcot BG, Hanea AM. What is an optimal value of k in k-fold cross validation in discrete Bayesian network analysis?. Computational Statistics 2020. doi: 10.1007/s00180-020-00999-9
[48] Quek M, Chin N, Yusof Y, Law C, Tan S. Pattern recognition analysis on nutritional profile and chemical composition of edible bird’s nest for its origin and authentication. International Journal Of Food Properties 2018; 21(1): 1680–1696.