Abstract
Image processing concepts can visualize the different anatomy structure of the human body. Recent advancements in the field of deep learning have made it possible to detect the growth of cancerous tissue just by a patient’s brain magnetic resonance imaging (MRI) scans. These methods require very high accuracy and meager false-negative rates to be of any practical use. This paper presents a convolutional neural network (CNN)-based transfer learning approach to classify the brain MRI scans into two classes using three pre-trained models. The performances of these models are compared with each other. Experimental results show that the Resnet-50 model achieves the highest accuracy and least false-negative rates as 95% and 0, respectively. It is followed by VGG-16 and Inception-V3 model with an accuracy of 90% and 55%, respectively.
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References
H. Ritchie, How Many People in the World Die from Cancer? Our World in Data, Institute for Health Metrics and Evaluation (IHME). https://ourworldindata.org/how-many-people-in-the-world-die-from-cancer. 1 Feb. 2018
S. Thrun, L. Pratt (eds.) Learning to Learn (Springer Science & Business Media, Berlin, 2012)
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
C. Szegedy et al., Rethinking the inception architecture for computer vision, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
K. He et al., Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
M.M. Beno et al., Threshold prediction for segmenting tumour from brain MRI scans. Int. J. Imaging Syst. Technol. 24(2), 129–137 (2014)
E.A. El-Dahshan et al., Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Syst. Appl. 41(11), 5526–5545 (2014)
K.E. Emblem et al., Predictive modeling in glioma grading from MR perfusion images using support vector machines. Mag Reson. Med.: Off. J. Int. Soc. Mag. Resona. Med. 60(4), 945–952 (2008)
M. Rahmani, G. Akbarizadeh, Unsupervised feature learning based on sparse coding and spectral clustering for segmentation of synthetic aperture radar images. IET Comput. Vision 9(5), 629–638 (2015)
P.D. Chang, Fully convolutional deep residual neural networks for brain tumor segmentation, in International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Springer, Cham, 2016)
O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, Cham, 2015)
O. Cicek et al., 3D U-Net: learning dense volumetric segmentation from sparse annotation, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, Cham, 2016)
M. Lai, Deep learning for medical image segmentation. arXiv preprint arXiv:1505.02000 (2015)
N. Chakrabarty, Brain MRI Images for Brain Tumor Detection, Kaggle. https://www.kaggle.com/navoneel/brain-mri-images-for-braintumor-detection. 14 Apr 2019
A. Rosebrock, Finding Extreme Points in Contours with OpenCV. PyImageSearch, https://www.pyimagesearch.com/2016/04/11/finding-extremepoints-in-contours-with-opencv/. 11 2016
A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. (2012)
M.A. Tanner, W.H. Wong, The calculation of posterior distributions by data augmentation. J. Am. Statist. Assoc. 82(398), 528–540 (1987)
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Saxena, P., Maheshwari, A., Maheshwari, S. (2021). Predictive Modeling of Brain Tumor: A Deep Learning Approach. In: Sharma, M.K., Dhaka, V.S., Perumal, T., Dey, N., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision. Advances in Intelligent Systems and Computing, vol 1189. Springer, Singapore. https://doi.org/10.1007/978-981-15-6067-5_30
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DOI: https://doi.org/10.1007/978-981-15-6067-5_30
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