Journal
MATHEMATICAL BIOSCIENCES AND ENGINEERING
Volume 17, Issue 5, Pages 6203-6216Publisher
AMER INST MATHEMATICAL SCIENCES-AIMS
DOI: 10.3934/mbe.2020328
Keywords
brain tumor; MRI; deep learning; CNN; transfer learning; VGG; inception; resnet
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Funding
- project of manned space engineering technology(2018-14) Development of large-scale spacecraft flight and reentry surveillance and prediction system
- National Natural Science Foundation of China [91530319]
- Southwest University of Science and Technology [13ZX7102]
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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 pretrained 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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