Brain tumor classification in MRI image using convolutional neural network

Hassan Ali Khan; Wu Jue; Muhammad Mushtaq; Muhammad Umer Mushtaq; Hassan Ali Khan; Wu Jue; Muhammad Mushtaq; Muhammad Umer Mushtaq

doi:10.3934/mbe.2020328

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 5: 6203-6216. doi: 10.3934/mbe.2020328

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Brain tumor classification in MRI image using convolutional neural network

1.
School of Computer Science and Technology, Southwest Unversity of Science and Techonlogy, Mianyang 621010, China
2.
Insitute for Neuro and Bioinformatics, University of Lübeck, Germany
3.
School of Information Engineering, Southwest Unversity of Science and Techonlogy, Mianyang 621010, China
4.
Department of Software Engineering, MUST, Mirpur AJK, Pakistan

Received: 08 July 2020 Accepted: 31 August 2020 Published: 15 September 2020

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Recent progress in the field of deep learning has helped the health industry in Medical Imaging for Medical Diagnostic of many diseases. For Visual learning and Image Recognition, task CNN is the most prevalent and commonly used machine learning algorithm. Similarly, in our paper, we introduce the convolutional neural network (CNN) approach along with Data Augmentation and Image Processing to categorize brain MRI scan images into cancerous and non-cancerous. Using the transfer learning approach we compared the performance of our scratched CNN model with pre-trained VGG-16, ResNet-50, and Inception-v3 models. As the experiment is tested on a very small dataset but the experimental result shows that our model accuracy result is very effective and have very low complexity rate by achieving 100% accuracy, while VGG-16 achieved 96%, ResNet-50 achieved 89% and Inception-V3 achieved 75% accuracy. Our model requires very less computational power and has much better accuracy results as compared to other pre-trained models.
- brain tumor,
- MRI,
- deep learning,
- CNN,
- transfer learning,
- VGG,
- inception,
- resnet
Citation: Hassan Ali Khan, Wu Jue, Muhammad Mushtaq, Muhammad Umer Mushtaq. Brain tumor classification in MRI image using convolutional neural network[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 6203-6216. doi: 10.3934/mbe.2020328

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Abstract

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Recent progress in the field of deep learning has helped the health industry in Medical Imaging for Medical Diagnostic of many diseases. For Visual learning and Image Recognition, task CNN is the most prevalent and commonly used machine learning algorithm. Similarly, in our paper, we introduce the convolutional neural network (CNN) approach along with Data Augmentation and Image Processing to categorize brain MRI scan images into cancerous and non-cancerous. Using the transfer learning approach we compared the performance of our scratched CNN model with pre-trained VGG-16, ResNet-50, and Inception-v3 models. As the experiment is tested on a very small dataset but the experimental result shows that our model accuracy result is very effective and have very low complexity rate by achieving 100% accuracy, while VGG-16 achieved 96%, ResNet-50 achieved 89% and Inception-V3 achieved 75% accuracy. Our model requires very less computational power and has much better accuracy results as compared to other pre-trained models.

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