Brain Tumor Identification Based on VGG-16 Architecture and CLAHE Method

Suci Aulia - Telkom University, Bandung, Indonesia
Dadi Rahmat - Bandung Institute of Technology, Bandung, Indonesia


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DOI: http://dx.doi.org/10.30630/joiv.6.1.864

Abstract


Magnetic Resonance Imaging (MRI) in diagnosing brain cancers is widespread. Because of the variety of angles and clarity of anatomy, it is commonly employed. If a brain tumor is malignant or secondary, it is a high risk, leading to death. These tumors have an increased predisposition for spreading from one place to another. In detecting brain abnormality form such as a tumor, from a magnetic resonance scan, expertise and human involvement are required. Previous, the image segmentation of brain tumors is widely developed in this field. Suppose we could somehow use an automatic brain tumor detection technology to identify the presence of a tumor in the brain without requiring human intervention. In that case, it will give us a leg up in the treatment process. This research proposed two stages to identify the brain tumor in MRI; the first stage was the image enhancement process using Clip Limit Adaptive Histogram Equalization (CLAHE) to segment the brain MRI. The second one was classifying the brain tumor on MRI using Visual Geometry Group-16 Layer (VGG-16). The CLAHE was used in some instances, there were CLAHE applied in FLAIR image on green color, and CLAHE applied in Red, Green, Blue (RGB) color space. The experimental result showed the highest performance with accuracy, precision, recall, respectively 90.37%, 90.22%, 87.61%. The CLAHE method in RGB Channel and the VGG-16 model have reliably on predicted oligodendroglioma classes in RGB enhancement with precision 91.08% and recall 95.97%.

Keywords


Brain Tumor; Magnetic Resonance Imaging; CLAHE; VGG-16; deep learning.

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References


I. Wahlang, P. Sharma, S. Sanyal, G. Saha, and A. K. Maji, “Deep learning techniques for classification of brain MRI,†Int. J. Intell. Syst. Technol. Appl., vol. 19, no. 6, pp. 571–588, 2020, doi: 10.1504/IJISTA.2020.112441.

H. W. Goo and Y.-S. Ra, “Advanced MRI for Pediatric Brain Tumors with Emphasis on Clinical Benefits,†Korean J. Radiol., vol. 18, no. 1, p. 194, 2017, doi: 10.3348/kjr.2017.18.1.194.

I. Wahlang, P. Sharma, S. Sanyal, G. Saha, and A. K. Maji, “Deep learning techniques for classification of brain MRI,†Int. J. Intell. Syst. Technol. Appl., vol. 19, no. 6, p. 571, 2020, doi: 10.1504/IJISTA.2020.112441.

N. H. Apriantoro and Christianni, “Analisis Perbedaan Citra MRI Brain Pada Sekuenti1se dan T1flair,†SINERGI, vol. 19, no. 3, pp. 206–210, 2015.

H. Kaur and J. Rani, “MRI brain image enhancement using Histogram Equalization techniques,†in 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Mar. 2016, no. 1, pp. 770–773, doi: 10.1109/WiSPNET.2016.7566237.

V. Stimper, S. Bauer, R. Ernstorfer, B. Scholkopf, and R. P. Xian, “Multidimensional Contrast Limited Adaptive Histogram Equalization,†IEEE Access, vol. 7, pp. 165437–165447, 2019, doi: 10.1109/ACCESS.2019.2952899.

M. S. Maheshan, B. S. Harish, and N. Nagadarshan, “On the use of Image Enhancement Technique towards Robust Sclera Segmentation,†Procedia Comput. Sci., vol. 143, pp. 466–473, 2018, doi: 10.1016/j.procs.2018.10.419.

J. Ma, X. Fan, S. X. Yang, X. Zhang, and X. Zhu, “Contrast Limited Adaptive Histogram Equalization-Based Fusion in YIQ and HSI Color Spaces for Underwater Image Enhancement,†Int. J. Pattern Recognit. Artif. Intell., vol. 32, no. 07, p. 1854018, Jul. 2018, doi: 10.1142/S0218001418540186.

H. K. Buddha, J. S. Meka, and P. Choppala, “OCR Image Enhancement & Implementation by using CLAHE algorithm,†Mukt Shabd J., vol. IX, no. IV April, pp. 3595–3599, 2020.

M. Sepasian, W. Balachandran, and C. Mares, “Image Enhancement for Fingerprint Minutiae-Based Algorithms Using CLAHE, Standard Deviation Analysis and Sliding Neighborhood,†Lect. Notes Eng. Comput. Sci., vol. 2173, no. 1, pp. 1199–1203, 2008.

Erwin, R. P. Sari, G. R. Utami, and A. N. Harison, “Enhancement Citra Fundus Retina Menggunakan CLAHE dan Wiener Filter,†in Prosiding Annual Research Seminar 2018 Computer Science and ICT ISBN, 2018, vol. 4, no. 1, pp. 978–979.

P. Musa, F. Al Rafi, and M. Lamsani, “A Review: Contrast-Limited Adaptive Histogram Equalization (CLAHE) methods to help the application of face recognition,†in 2018 Third International Conference on Informatics and Computing (ICIC), Oct. 2018, no. October, pp. 1–6, doi: 10.1109/IAC.2018.8780492.

A. Elnakib, H. M. Amer, and F. E. Z. Abou-Chadi, “Early lung cancer detection using deep learning optimization,†Int. J. online Biomed. Eng., vol. 16, no. 6, pp. 82–94, 2020, doi: 10.3991/ijoe.v16i06.13657.

D. Bendarkar, P. Somase, P. Rebari, R. Paturkar, and A. Khan, “Web Based Recognition and Translation of American Sign Language with CNN and RNN,†Int. J. online Biomed. Eng., vol. 17, no. 1, pp. 34–50, 2021, doi: 10.3991/ijoe.v17i01.18585.

B. R. Nanditha, A. G. Kiran, H. S. Chandrashekar, M. S. Dinesh, and S. Murali, “An Ensemble Deep Neural Network Approach for Oral Cancer Screening,†Int. J. online Biomed. Eng., vol. 17, no. 2, pp. 121–134, 2021, doi: 10.3991/ijoe.v17i02.19207.

Mohsen H., El-Dahsan E. A., El-Horbaty E. M., and M. Salem A. “Classification using deep learning neural networks for brain tumors,†Future Computing and Informatics Journal, vol. 3, pp. 68-71, 2018.

W. S. Prakoso, I. Soesanti, and S. Wibirama, “Enhancement methods of brain MRI images: A Review,†2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE), 2020.

T. Kaur and T. K. Gandhi, “Automated Brain Image Classification based on VGG-16 and transfer learning,†2019 International Conference on Information Technology (ICIT), 2019.

M. Buda, E. A. AlBadawy, A. Saha, and M. A. Mazurowski, “Deep radiogenomics of lower-grade gliomas: Convolutional neural networks predict tumor genomic subtypes using mr images,†Radiology: Artificial Intelligence, vol. 2, no. 1, 2020.

S. M. Pizer, R. E. Johnston, J. P. Ericksen, B. C. Yankaskas, and K. E. Muller, “Contrast-limited adaptive histogram equalization: speed and effectiveness,†in [1990] Proceedings of the First Conference on Visualization in Biomedical Computing, 1990, pp. 337–345, doi: 10.1109/VBC.1990.109340.

R. M. Yanni, N. E.-K. El-Ghitany, K. Amer, A. Riad, and H. El-Bakry, “A new model for image segmentation based on Deep Learning,†International Journal of Online and Biomedical Engineering (iJOE), vol. 17, no. 07, p. 28, 2021.

S. Saifullah, “Analisis Perbandingan HE dan CLAHE pada Image Enhancement dalam Proses Segmenasi Citra untuk Deteksi Fertilitas Telur,†J. Nas. Pendidik. Tek. Inform., vol. 9, no. 1, p. 134, Apr. 2020, doi: 10.23887/janapati.v9i1.23013.

J. Joseph, J. Sivaraman, R. Periyasamy, and V. R. Simi, “An objective method to identify optimum clip-limit and histogram specification of contrast limited adaptive histogram equalization for MR images,†Biocybernetics and Biomedical Engineering, vol. 37, no. 3, pp. 489–497, 2017.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137–1149, Jun. 2017, doi: 10.1109/TPAMI.2016.2577031.

R. Girshick, “Fast R-CNN,†in 2015 IEEE International Conference on Computer Vision (ICCV), Dec. 2015, vol. 2015 Inter, pp. 1440–1448, doi: 10.1109/ICCV.2015.169.

A. Samreen, A. M. Taha, Y. V. Reddy, and S. P, “Brain tumor detection by using Convolution Neural Network,†International Journal of Online and Biomedical Engineering (iJOE), vol. 16, no. 13, p. 58, 2020.