Medical diagnosis via image processing and machine learning is considered one of the most important issues of artificial intelligence systems. In this paper, we present a machine learning approach to detect whether an MRI image of a brain contains a tumour or not. The results show that such an approach is very promising. Automated detection of tumor in Magnetic Resonance Imaging (MRI) is very crucial as it provides information about abnormal tissues, which is necessary for planning treatment. Deep Learning is a new machine-learning arena that increased a lot of attention over the earlier few ages. It was extensively useful to numerous bids and established to be an influential machine-learning tool for many of the complex difficulties. In this paper, we used Deep Neural Network classifier, which is one of the DL architectures for classifying a dataset of 66 brain MRIs into four classes e.g. normal, glioblastoma, sarcoma and metastatic bronchogenic carcinoma tumours. The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool and principal components analysis (PCA) and the assessment of the presentation was quite good over all the presentation measures.

Machine learning, Deep learning, Deep neural network, Discrete wavelet transform, Principle component analysis, Fuzzy c-means, Magnetic resonance images

[1] A.R. Kavitha, L. Chitra, R. kanaga “Brain tumor segmentation using genetic algorithm with SVM classifier”, Int J Adv Res Electr Electron Instrum Eng, 5 (3) (2016).
[2] V.S. Ramachandran, (Sandra Blakeslee), “Phantoms in the Brain: Human Nature and the Architecture of the Mind Fourth Dimension Publications ISBN: 1857028953, 1999.
[3] Kruti G. Khambhata, Sandip R. Panchal, “Multiclass classification of brain tumor in MR Images”, Int J Innov Res Comput Commun Eng, 4 (5), 2016, pp. 8982-8992.
[4] G. Kaur, J. Rani, “MRI brain tumor segmentation methods-a review”, Int J Comput Eng Technol (IJCET), 6 (3), 2016, pp. 760-764.
[5] V. Das, J. Rajan, “Techniques for MRI brain tumor detection: a survey”, Int J Res Comput Appl Inf Tech, 4 (3), 2016, pp. 53-56.
[6] E.I Zacharaki, S. Wang, S. Chawla, D. Soo Yoo R. Wolf, E.R. Melhem, “Classification of brain tumor type and grade using MRI texture and shape in machine learning scheme”, Magn Reson Med, 62, 2009, pp. 1609-1618.
[7] G.Litjens, T. Kooi, B.E. Bejnordi, A.A. Setio, F. Ciompi, M. Ghafoorian, “A survey on deep learning in medical image analysis”, Med Image Anal, 42, 2017, pp. 60-88.
[8] L. Singh, G. Chetty, D. Sharma, “A novel machine learning approach for detecting the brain abnormalities from MRI structural images” IAPR international conference on pattern recognition in bioinformatics, Springer, Berlin Heidelberg (2012), pp. 94-105
[9] Y. Pan, W. Huang, Z. Lin, W. Zhu, J. Zhou, J. Wong, et al “Brain tumor grading based on neural networks and convolutional neural networks Engineering in medicine and biology society (EMBC), 37th annual international conference of the IEEE (2015), pp. 699-702.
[10] Ravì, C. Wong, F. Deligianni, M. Berthelot, J. Andreu- Perez, B. Lo, et al, “Deep learning for health informatics”, IEEE J Biomed Health Inf, 21 (1) (2017), pp. 4-21