Convolutional Neural Network featuring VGG-16 Model for Glioma Classification

Agus Minarno - Universitas Muhammadiyah Malang, Malang, Indonesia
Sasongko Bagas - Universitas Muhammadiyah Malang, Malang, Indonesia
Munarko Yuda - Universitas Muhammadiyah Malang, Malang, Indonesia
Nugroho Hanung - Universitas Gadjah Mada, Yogyakarta, Indonesia
Zaidah Ibrahim - Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.6.3.1230

Abstract


Magnetic Resonance Imaging (MRI) is a body sensing technique that can produce detailed images of the condition of organs and tissues. Specifically related to brain tumors, the resulting images can be analyzed using image detection techniques so that tumor stages can be classified automatically. Detection of brain tumors requires a high level of accuracy because it is related to the effectiveness of medical actions and patient safety. So far, the Convolutional Neural Network (CNN) or its combination with GA has given good results. For this reason, in this study, we used a similar method but with a variant of the VGG-16 architecture. VGG-16 variant adds 16 layers by modifying the dropout layer (using softmax activation) to reduce overfitting and avoid using a lot of hyper-parameters. We also experimented with using augmentation techniques to anticipate data limitations. Experiment using data The Cancer Imaging Archive (TCIA) - The Repository of Molecular Brain Neoplasia Data (REMBRANDT) contains MRI images of 130 patients with different ailments, grades, races, and ages with 520 images. The tumor type was Glioma, and the images were divided into grades II, III, and IV, with the composition of 226, 101, and 193 images, respectively. The data is divided by 68% and 32% for training and testing purposes. We found that VGG-16 was more effective for brain tumor image classification, with an accuracy of up to 100%. 


Keywords


Classification; MRI; brain tumor; Glioma, CNN; VGG-16.

Full Text:

PDF

References


R. J. Young and E. A. Knopp, “Brain MRI: Tumor evaluation,†Journal of Magnetic Resonance Imaging, vol. 24, no. 4, pp. 709–724, Oct. 2006, doi: 10.1002/JMRI.20704.

A. Wadhwa, A. Bhardwaj, V. V.-M. resonance imaging, and undefined 2019, “A review on brain tumor segmentation of MRI images,†Elsevier, Accessed: Jun. 13, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0730725X19300347

Z. Hu et al., “Deep learning for image-based cancer detection and diagnosis− A survey,†Elsevier, Accessed: Jun. 13, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0031320318301845

H. Mohsen, E. El-Dahshan, … E. E.-H.-F. C. and, and undefined 2018, “Classification using deep learning neural networks for brain tumors,†Elsevier, Accessed: Jun. 13, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2314728817300636

H. Sultan, N. Salem, W. A.-A.-I. Access, and undefined 2019, “Multi-classification of brain tumor images using deep neural network,†ieeexplore.ieee.org, Accessed: Jun. 13, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8723045/

G. Mohan, M. S.-B. S. P. and Control, and undefined 2018, “MRI based medical image analysis: Survey on brain tumor grade classification,†Elsevier, Accessed: Jun. 13, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1746809417301398

J. Amin, M. Sharif, M. Yasmin, S. F.-P. R. Letters, and undefined 2020, “A distinctive approach in brain tumor detection and classification using MRI,†Elsevier, Accessed: Jun. 13, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S016786551730404X

M. Mittal, L. Goyal, S. Kaur, I. Kaur, … A. V.-A. S., and undefined 2019, “Deep learning based enhanced tumor segmentation approach for MR brain images,†Elsevier, Accessed: Jun. 13, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1568494619301000

A. Tiwari, S. Srivastava, M. P.-P. R. Letters, and undefined 2020, “Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019,†Elsevier, Accessed: Jun. 13, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S016786551930340X

N. Gordillo, E. Montseny, P. S.-M. resonance imaging, and undefined 2013, “State of the art survey on MRI brain tumor segmentation,†Elsevier, Accessed: Jun. 13, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0730725X13001872

C. Szegedy et al., “Going Deeper with Convolutions,†Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 07-12-June-2015, pp. 1–9, Sep. 2014, doi: 10.48550/arxiv.1409.4842.

M. Lin, Q. Chen, and S. Yan, “Network In Network,†2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings, Dec. 2013, doi: 10.48550/arxiv.1312.4400.

G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,†arxiv.org, Accessed: Jun. 13, 2022. [Online]. Available: https://arxiv.org/abs/1207.0580

T. F. Gonzalez, “Handbook of approximation algorithms and metaheuristics,†Handbook of Approximation Algorithms and Metaheuristics, pp. 1–1432, Jan. 2007, doi: 10.1201/9781420010749/HANDBOOK-APPROXIMATION-ALGORITHMS-METAHEURISTICS-TEOFILO-GONZALEZ.

A. Toshev, … C. S. the I. conference on computer, and undefined 2014, “Deeppose: Human pose estimation via deep neural networks,†openaccess.thecvf.com, Accessed: Jun. 13, 2022. [Online]. Available: http://openaccess.thecvf.com/content_cvpr_2014/html/Toshev_DeepPose_Human_Pose_2014_CVPR_paper.html

E. I. Zacharaki et al., “Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme,†Magnetic Resonance in Medicine, vol. 62, no. 6, pp. 1609–1618, 2009, doi: 10.1002/MRM.22147.

Q. Guan et al., “Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study,†ncbi.nlm.nih.gov, Accessed: Jun. 13, 2022. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/pmc6775529/

S. Banerjee, S. Member, I. Supervisor, F. Masulli, S. Member, and S. Mitra, “Brain Tumor Detection and Classification from Multi-Channel MRIs using Deep Learning and Transfer Learningâ€.

K. Clark et al., “The cancer imaging archive (TCIA): Maintaining and operating a public information repository,†Journal of Digital Imaging, vol. 26, no. 6, pp. 1045–1057, Dec. 2013, doi: 10.1007/S10278-013-9622-7.

“Wiki - The Cancer Imaging Archive (TCIA) Public Access - Cancer Imaging Archive Wiki.†https://wiki.cancerimagingarchive.net/ (accessed Jun. 13, 2022).

A. Rehman and T. Saba, “Neural networks for document image preprocessing: State of the art,†Artificial Intelligence Review, vol. 42, no. 2, pp. 253–273, 2014, doi: 10.1007/S10462-012-9337-Z.

K. Gopalakrishnan, S. Khaitan, A. C.-… and building materials, and undefined 2017, “Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection,†Elsevier, Accessed: Jun. 13, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0950061817319335

A. Kabir Anaraki, M. Ayati, and F. Kazemi, “Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms,†Biocybernetics and Biomedical Engineering, vol. 39, no. 1, pp. 63–74, Jan. 2019, doi: 10.1016/J.BBE.2018.10.004.