TY - JOUR
T1 - Brain tumor classification in MRI image using convolutional neural network
AU - Khan, Hassan Ali
AU - Jue, Wu
AU - Mushtaq, Muhammad
AU - Mushtaq, Muhammad
N1 - Funding Information:
This work is supported by the project of manned space engineering technology(2018-14) Development of large-scale spacecraft flight and reentry surveillance and prediction system, and the National Natural Science Foundation of China (91530319), and the doctoral fund of the Southwest University of Science and Technology(13ZX7102).
Publisher Copyright:
© 2020 American Institute of Mathematical Sciences. All rights reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/9/15
Y1 - 2020/9/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85092566113&partnerID=8YFLogxK
U2 - 10.3934/MBE.2020328
DO - 10.3934/MBE.2020328
M3 - Journal articles
C2 - 33120595
AN - SCOPUS:85092566113
SN - 1547-1063
VL - 17
SP - 6203
EP - 6216
JO - Mathematical Biosciences and Engineering
JF - Mathematical Biosciences and Engineering
IS - 5
ER -
