Classification of Brain MRI Using Deep Learning Techniques

MR images are popularly used as a tool in diagnosis of brain tumors. It is widely used because of its varieties of angles and clarity of anatomy. Brain tumor is dangerous if it is malignant or secondary tumor. These kinds of tumor can easily spread from one location to another. Expertise and human intervention are needed to detect any kind of abnormalities like tumor, etc., from MR image. So, if we can use an automated brain tumor detection methodology to predict the presence of tumor in brain without human intervention, it will provide an edge in the process of treatment to this disease. Classification plays a vital role in detection of brain tumor. Taking into account the importance of detection of brain tumor, this paper analyzes four architectures of convolutional neural networks (CNN) for classification of brain MR images into tumorous or nontumorous in unsupervised manner. The architectures which are discussed in this paper are ConvNet, Lenet, ResNet, and Densenet.

[1]  T Karthick,et al.  A Novel Study of Machine Learning Algorithms for Classifying Health Care Data , 2017 .

[2]  Vikul J. Pawar,et al.  Feature extraction and selection from MRI images for the brain tumor classification , 2016, 2016 International Conference on Communication and Electronics Systems (ICCES).

[3]  Victor Alves,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2016, IEEE Transactions on Medical Imaging.

[4]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  S. Das,et al.  Effective classification of radiographic medical images using LS-SVM and NSCT based retrieval system , 2012, 2012 5th International Conference on Computers and Devices for Communication (CODEC).

[6]  V. Mareeswari,et al.  Prediction of Diabetes Using Data Mining Techniques , 2017 .

[8]  R. Suja Mani Malar,et al.  Rough set theory and feed forward neural network based brain tumor detection in magnetic resonance images , 2013, International Conference on Advanced Nanomaterials & Emerging Engineering Technologies.

[9]  Anupam Shukla,et al.  Detection and Classification of MRI Brain Images For Head/Brain Injury Using Soft Computing Techniques , 2017 .

[10]  Anita Agrawal,et al.  Hybrid approach for brain tumor detection and classification in magnetic resonance images , 2015, 2015 Communication, Control and Intelligent Systems (CCIS).

[11]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[12]  Weimin Huang,et al.  Brain tumor grading based on Neural Networks and Convolutional Neural Networks , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[13]  Fang Zhang,et al.  Deep convolutional activation features for large scale Brain Tumor histopathology image classification and segmentation , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[16]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..