Deep Learning Approach for Prediction of Brain Tumor from Small Number of MRI Images

Zulaikha N.I. Zailan - Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Johor, Malaysia
Salama A. Mostafa - Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Johor, Malaysia
Alyaa Idrees Abdulmaged - Universiti Tun Hussein Onn Malaysia Pagoh Campus, 84600 Pagoh, Johor, Malaysia
Zirawani Baharum - Universiti Kuala Lumpur, Persiaran Sinaran Ilmu, 81750 Johor Bahru, Malaysia
Mustafa Musa Jaber - Dijlah University College, Baghdad,10021, Iraq
Rahmat Hidayat - Department of Information Technology, Politeknik Negeri Padang, Sumatera Barat, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.6.2.987

Abstract


Daily, the computer industry has been moving towards machine intelligence. Deep learning is a subfield of artificial intelligence (AI)'s machine learning (ML). It has AI features that mimic the functioning of the human brain in analyzing data and generating patterns for making decisions. Deep learning is gaining much attention nowadays because of its superior precision when trained with large data. This study uses the deep learning approach to predict brain tumors from medical images of magnetic resonance imaging (MRI). This study is conducted based on CRISP-DM methodology using three deep learning algorithms: VGG-16, Inception V3, MobileNet V2, and implemented by the Python platform. The algorithms predict a small number of MRI medical images since the dataset has only 98 image samples of benign and 155 image samples of malignant brain tumors. Subsequently, the main objective of this work is to identify the best deep learning algorithm that performs on small-sized datasets. The performance evaluation results are based on the confusion matrix criteria, accuracy, precision, and recall, among others. Generally, the classification results of the MobileNet-V2 tend to be higher than the other models since its recall value is 86.00%. For Inception-V3, it got the second highest accuracy, 84.00%, and the lowest accuracy is VGG-16 since it got 79.00%. Thus, in this work, we show that DL technology in the medical field can be more advanced and easier to predict brain tumors, even with a small dataset.

Keywords


Brain tumor; magnetic resonance imaging; deep learning; image classification.

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References


T. F. Drumond, T. Viéville, and F. Alexandre, “Bio-inspired analysis of deep learning on not-so-big data using data-prototypes,†Frontiers in computational neuroscience, 12, 100, 2019.

M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic, "Deep learning applications and challenges in big data analytics," J. Big Data, vol. 2, no. 1, pp. 1–21, 2015.

C. Gleason and S. Devaskar, "Brain Tumors," Brain Tumors, vol. 344, no. 2, pp. 114–123, 2012.

S. V Nallamala, S. H., Mishra, P., & Koneru, "Breast Cancer Detection using Machine Learning Way," Int. J. Recent Technol. Eng., 2019.

Y. M. Saad, A. E., Elsayed, A. R., Mahmoud, S. E., & Elkheshen, "Breast Cancer Detection Using Machine Learning.," 2020.

M. Bharati, S., Podder, P., & Mondal, "Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review," arXiv Prepr. arXiv, vol. 2006.01767, 2020.

M. E. Paoletti, J. M. Haut, J. Plaza, and A. Plaza, "A new deep convolutional neural network for fast hyperspectral image classification," ISPRS J. Photogramm. Remote Sens., vol. 145, pp. 120–147, 2018.

G. Masi, I., Trần, A. T., Hassner, T., Leksut, J. T., & Medioni, "Do we really need to collect millions of faces for effective face recognition?," Eur. Conf. Comput. vision. Springer, Cham, pp. 579–596, 2016.

S. A. Badarudin, P. M., Ghazali, R., Alahdal, A. M. A., Alduais, N. A. M., & Mostafa, "Classification of Breast Cancer Patients Using Neural Network Technique," J. Soft Comput. Data Min., vol. 2, no. 1, pp. 13–19, 2021.

T. R. Razzaq, H. H., Ghazali, R., George, L. E., Mostafa, S. A., Al-Janabi, A. A., Fadel, A. H., ... & Hamza, "Empirical Analysis of a New Immunohistochemical Breast Cancer Images Dataset," Des. Eng., pp. 21–36, 2021.

O. Kim-Soon, N., Abdulmaged, A. I., Mostafa, S. A., Mohammed, M. A., Musbah, F. A., Ali, R. R., & Geman, "A framework for analyzing the relationships between cancer patient satisfaction, nurse care, patient attitude, and nurse attitude in healthcare systems," J. Ambient Intell. Humaniz. Comput., pp. 1–18, 2021.

N. Razali, S. A. Mostafa, A. Mustapha, M. H. A. Wahab, and N. A. Ibrahim, "Risk Factors of Cervical Cancer using Classification in Data Mining," J. Phys. Conf. Ser., vol. 1529, no. 2, 2020.

S. M. Saxena, Priyansh, Akshat Maheshwari, "Predictive modeling of brain tumor: A Deep learning approach," in Innovations in Computational Intelligence and Computer Vision, Springer, 2021, pp. 275–285.

S. Biswas, "Automatic Brain Tumor Detection and Classification On Mri Images Using Machine Learning Techniques," Maulana Abul Kalam Azad University of Techn," 2020.

D. D. Macdonald and G. R. Engelhardt, "Predictive modeling of corrosion," Shreir's Corros., no. January, pp. 1630–1679, 2010.

Y. Bazi, M. M. A. Rahhal, H. Alhichri, and N. Alajlan, "Simple yet effective fine-tuning of deep cnns using an auxiliary classification loss for remote sensing scene classification," Remote Sens., vol. 11, no. 24, 2019.

Nalawade, S., Murugesan, G.K., Vejdani-Jahromi, M., Fisicaro, R.A., Yogananda, C.G.B., Wagner, B., Mickey, B., Maher, E., Pinho, M.C., Fei, B. and Madhuranthakam, A.J. "Classification of brain tumor isocitrate dehydrogenase status using MRI and deep learning," J. Med. Imaging, 2019.

L. Chato and S. Latifi, "Machine Learning and Deep Learning Techniques to Predict Overall Survival of Brain Tumor Patients Using MRI Images," Proc. - 2017 IEEE 17th Int. Conf. Bioinforma. Bioeng. BIBE 2017, vol. 2018-Janua, no. October 2017, pp. 9–14, 2017.

J. Stember and H. Shalu, " Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images." arXiv preprint arXiv:2008.02708, 2020.

P. Afshar, A. Mohammadi, and K. N. Plataniotis, "Bayescap: A bayesian approach to brain tumor classification using capsule networks," IEEE Signal Process. Lett., vol. 27, pp. 2024–2028, 2020.

A. Azevedo and M. F. Santos, “KDD , SEMMA AND CRISP-DM : A parallel overview Ana Azevedo and M . F . Santos,†IADIS Eur. Conf. Data Min., pp. 182–185, 2008.

B. S. Meeting and P. Chapman, "The CRISP-DM User Guide," Cris. User Guid., p. 14, 1999.

S. Moro, R. M. S. Laureano, and P. Cortez, "Using data mining for bank direct marketing: An application of the CRISP-DM methodology," ESM 2011 - 2011 Eur. Simul. Model. Conf. Model. Simul. 2011, pp. 117–121, 2011.

N. Chakrabarty "Brain MRI images for brain tumor detection.", (2019, April 14). Retrieved May 09, 2020, from https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection.

K.,Simonyan, & A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.

Q. Guan, Y. Wang, B. Ping, D. Li, J. Du, Y. Qin, and J. Xiang, "Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study," Journal of Cancer, 10(20), 4876, 2019.

C. A. Hartanto, and L. Rahadianti, “Single Image Dehazing Using Deep Learning,†JOIV: International Journal on Informatics Visualization, 5(1), 76-82, 2021.

Z. Liu, C. Yang, J. Huang, S. Liu, Y. Zhuo, and X. Lu, “Deep learning framework based on integration of S-Mask R-CNN and Inception-v3 for ultrasound image-aided diagnosis of prostate cancer,†Future Generation Computer Systems, 114, 358-367, 2021.

A. B. M. Wijaya, D. S. Ikawahyuni, R. Gea, and F. Maedjaja, “Role Comparison between Deep Belief Neural Network and Neuro Evolution of Augmenting Topologies to Detect Diabetes,†JOIV: International Journal on Informatics Visualization, 5(2), 156-161, 2021.

P. N. Srinivasu, J. G. SivaSai, M. F. Ijaz, A. K. Bhoi, W. Kim, and J. J. Kang, “Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM,†Sensors, 21(8), 2852, 2021.

M., Elhoseny, M. A., Mohammed, S. A., Mostafa, K. H., Abdulkareem, M. S., Maashi, B., Garcia-Zapirain, and M. S. Maashi, “A new multi-agent feature wrapper machine learning approach for heart disease diagnosis,†Comput. Mater. Contin, 67, 51-71, 2021.

G. S. Saragih, Z. Rustam, D. Aldila, R. Hidayat, R. E. Yunus, and J. Pandelaki, “Ischemic Stroke Classification using Random Forests Based on Feature Extraction of Convolutional Neural Networks. International Journal on Advanced Science, Engineering and Information Technology,†10(5), 2177-2182, 2020.

A. Hidaka and T. Kurita, "Consecutive Dimensionality Reduction by Canonical Correlation Analysis for Visualization of Convolutional Neural Networks," Proc. Isc. Int. Symp. Stoch. Syst. Theory its Appl., vol. 2017, no. 0, pp. 160–167, 2017.