An enhanced deep learning approach for brain cancer MRI images classification using residual networks

Cancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types depending on their shape, texture, and location. Proper diagnosis of the tumor type enables the doctor to make the correct treatment choice and help save the patient's life. There is a high need in the Artificial Intelligence field for a Computer Assisted Diagnosis (CAD) system to assist doctors and radiologists with the diagnosis and classification of tumors. Over recent years, deep learning has shown an optimistic performance in computer vision systems. In this paper, we propose an enhanced approach for classifying brain tumor types using Residual Networks. We evaluate the proposed model on a benchmark dataset containing 3064 MRI images of 3 brain tumor types (Meningiomas, Gliomas, and Pituitary tumors). We have achieved the highest accuracy of 99% outperforming the other previous work on the same dataset.

[1]  G. Pradeepini,et al.  Brain tumor classification using mixed method approach , 2017, 2017 International Conference on Information Communication and Embedded Systems (ICICES).

[2]  Jasjit S Suri,et al.  A Review on a Deep Learning Perspective in Brain Cancer Classification , 2019, Cancers.

[3]  Rosie Dunford,et al.  The Pareto Principle , 2014 .

[4]  Konstantinos N. Plataniotis,et al.  Brain Tumor Type Classification via Capsule Networks , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

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

[6]  Joachim M. Buhmann,et al.  The Balanced Accuracy and Its Posterior Distribution , 2010, 2010 20th International Conference on Pattern Recognition.

[7]  Daniel Fabbri,et al.  Deep learning for brain tumor classification , 2017, Medical Imaging.

[8]  Konstantinos N. Plataniotis,et al.  Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  P Babu Anto,et al.  Tumor detection and classification of MRI brain image using wavelet transform and SVM , 2017, 2017 International Conference on Signal Processing and Communication (ICSPC).

[10]  Michał Grochowski,et al.  Data augmentation for improving deep learning in image classification problem , 2018, 2018 International Interdisciplinary PhD Workshop (IIPhDW).

[11]  Asmita Moghe,et al.  Brain tumor detection and classification in MRI images using image and data mining , 2017, 2017 International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE).

[12]  G. Ramani,et al.  MR Brain Tumor Classification and Segmentation Via Wavelets , 2018, 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[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]  Mahmoud Khaled Abd-Ellah,et al.  Design and implementation of a computer-aided diagnosis system for brain tumor classification , 2016, 2016 28th International Conference on Microelectronics (ICM).

[15]  V. Balamurugan,et al.  Robust classification of primary brain tumor in Computer Tomography images using K-NN and linear SVM , 2014, 2014 International Conference on Contemporary Computing and Informatics (IC3I).

[16]  Ruwan D. Nawarathna,et al.  A sophisticated convolutional neural network model for brain tumor classification , 2017, 2017 IEEE International Conference on Industrial and Information Systems (ICIIS).

[17]  Steve B. Jiang,et al.  A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery , 2017, PloS one.

[18]  Tati L. R. Mengko,et al.  Brain Tumor Classification Using Convolutional Neural Network , 2018, IFMBE Proceedings.

[19]  S.N. Deepa,et al.  Neural networks and SMO based classification for brain tumor , 2011, 2011 World Congress on Information and Communication Technologies.

[20]  Sanjeev Kumar,et al.  Classification of Brain MRI Tumor Images: A Hybrid Approach , 2017, ITQM.

[21]  G. Reifenberger,et al.  The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary , 2016, Acta Neuropathologica.

[22]  Ghazanfar Latif,et al.  Multiclass brain Glioma tumor classification using block-based 3D Wavelet features of MR images , 2017, 2017 4th International Conference on Electrical and Electronic Engineering (ICEEE).

[23]  B. Sathees Kumar,et al.  Feature extraction using image mining techniques to identify brain tumors , 2015 .

[24]  Victor Alves,et al.  Understanding and Interpreting Machine Learning in Medical Image Computing Applications , 2018, Lecture Notes in Computer Science.

[25]  Dragica Radosav,et al.  Deep Learning and Medical Diagnosis: A Review of Literature , 2018, Multimodal Technol. Interact..

[26]  Jun Cheng,et al.  brain tumor dataset , 2016 .

[27]  J. Seetha,et al.  Brain Tumor Classification Using Convolutional Neural Networks , 2018, Biomedical and Pharmacology Journal.

[28]  K. Amshakala,et al.  An automated MRI brain image segmentation and tumor detection using SOM-clustering and Proximal Support Vector Machine classifier , 2015, 2015 IEEE International Conference on Engineering and Technology (ICETECH).

[29]  Qianjin Feng,et al.  Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition , 2015, PloS one.

[30]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[31]  Chandan Saha,et al.  A new hybrid approach for brain tumor classification using BWT-KSVM , 2017, 2017 4th International Conference on Advances in Electrical Engineering (ICAEE).

[32]  Keiron O'Shea,et al.  An Introduction to Convolutional Neural Networks , 2015, ArXiv.

[33]  Shiju Yan,et al.  A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology. , 2017, Journal of X-ray science and technology.

[34]  Ketan Machhale,et al.  MRI brain cancer classification using hybrid classifier (SVM-KNN) , 2015, 2015 International Conference on Industrial Instrumentation and Control (ICIC).

[35]  Namita Mittal,et al.  Performance analysis of Gabor-Wavelet based features in classification of high grade malignant brain tumors , 2015, 2015 39th National Systems Conference (NSC).

[36]  Hedieh Sajedi,et al.  Brain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines , 2018, 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE).

[37]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[38]  M. Sornam,et al.  Segmentation and classification of brain tumor using wavelet and Zernike based features on MRI , 2016, 2016 IEEE International Conference on Advances in Computer Applications (ICACA).

[39]  Vinod Kumar,et al.  Multiclass Brain Tumor Classification Using GA-SVM , 2011, 2011 Developments in E-systems Engineering.

[40]  Jianfeng Lu,et al.  Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning , 2019, IEEE Access.

[41]  P. Baldi,et al.  Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas , 2018, American Journal of Neuroradiology.

[42]  Y. M. Jang,et al.  Detection and classification of HGG and LGG brain tumor using machine learning , 2018, 2018 International Conference on Information Networking (ICOIN).

[43]  Christoph Meinel,et al.  Deep Neural Network with l2-Norm Unit for Brain Lesions Detection , 2017, ICONIP.

[44]  W. R. Sam Emmanuel,et al.  MRI Brain Tumor Classification Using Cuckoo Search Support Vector Machines and Particle Swarm Optimization Based Feature Selection , 2018 .

[45]  Antoanela Naaji,et al.  A Modified Deep Convolutional Neural Network for Abnormal Brain Image Classification , 2019, IEEE Access.

[46]  Chandrakant Mahobiya,et al.  MR Image Classification Using Adaboost for Brain Tumor Type , 2017, 2017 IEEE 7th International Advance Computing Conference (IACC).