Zheng Huang
ABSTRACT
Generally, the brain tumor is regarded as one of the most dangerous diseases. It is always too late to detect the brain tumors, as the tumors at the early stage are always ignored. In fact, the traditional manual diagnosis process is inefficient. The radiologists have to accomplish a great amount of reading work per day, which can result in weariness and thus lead to misdiagnosis. To liberate radiologists from endless work, a brain tumor screening system based on adaptive gamma correction and deep learning is proposed. The brain images are labeled with "non-tumor" and "tumors", and the radiologists just needs to deal with the brain images labeled with "tumors", which can significantly reduce the workload of the radiologists. Firstly, sufficient contrast enhanced T1-weighted brain images are collected. Further, background removal based on iterative threshold and a novel adaptive gamma correction (NAGC) are implemented to generate brain images with similar overall intensity. Finally, data augmentation technologies are applied to enlarge the training set, and convolutional neural network (CNN) is adopted to train the classifier. The results indicate that the accuracy of the proposed system can reach 95.13%.
- Mohan, G. and Subashini, M. M. 2018. MRI based medical image analysis: Survey on brain tumor grade classification. Biomedical Signal Processing and Control, 39, 139--161.Google Scholar
Cross Ref
- Bahadure, N. B., Ray, A. K., Thethi, H. P. 2018Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm. Journal of Digital Imaging, 31, 4, 447--489, 2018.Google ScholarCross Ref
- Bauer, S., Wiest, R., Nolte, L. P. and Reyes, M. 2013. A survey of mri-based medical image analysis for brain tumor studies. Physics in Medicine and Biology, 58, 13, 97--129.Google ScholarCross Ref
- Gupta, N. and Khanna, P. 2017. A non-invasive and adaptive CAD system to detect brain tumor from T2-weighted MRIs using customized Otsu's thresholding with prominent features and supervised learning. Signal Processing: Image Communication, 59, 18--26.Google ScholarCross Ref
- Anaraki, A. K., Ayati, M. and Kazemi, F. 2018. Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybernetics and Biomedical Engineering, 39, 63--74.Google ScholarCross Ref
- Qijia, H. E., Zhenbing, L., Tao, X. U. and Shujie, J. 2017. MR image classification based on LBP and extreme learning machine. Journal of Shandong University (Engineering Science), 47, 2, 86--93.Google Scholar
- Khalil, M., Ayad, H. and Adib, A. 2018. Performance evaluation of feature extraction techniques in MR-Brain image classification system. Procedia Computer Science, 127, 218--225.Google ScholarDigital Library
- Sajjad, S., Khan, S. and Muhammad, K. 2019. Multi-grade brain tumor classification using deep CNN with extensive data augmentation. Journal of Computational Science, 30, 174--182.Google ScholarCross Ref
- Qu, D., Huang, Z. and Gao, Z. 2018. An automatic system for smile recognition based on cnn and face detection. In proceedings of the IEEE International Conference on Robotics and Biomimetics, 243--247.Google Scholar
- Xie, J., Zhao, H. and Shao, Z. 2019. A fast approach for multi-modality surgical trajectory segmentation with unsupervised deep learning. Robot, 41, 3, 317--326.Google Scholar
- Lecun, Y., Bottou, L., Bengio, Y. and Haffner, P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 11, 2278--2324.Google ScholarCross Ref
- Simonyan, K. and Zisserman, A.(2014). Very deep convolutional networks for large-scale image recognition. arXiv: 1409. 1556 v6.Google Scholar
- Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S. and Anguelov, D. 2015. Going Deeper with Convolutions. In proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1--9.Google Scholar
- He, K., Zhang, X., Ren, S. and Sun, J. 2016. Deep Residual Learning for Image Recognition. In proceeding of IEEE Conference on Computer Vision and Pattern Recognition, 770--778.Google Scholar
- Nayak, D. R., Dash, R. and Majhi, B. 2015. Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing, 177, 188--197.Google ScholarDigital Library
Index Terms
- Brain Tumor Screening using Adaptive Gamma Correction and Deep Learning
Recommendations
Segmentation of Brain Tumor using Deep Learning Methods: A Review
DSMLAI '21': Proceedings of the International Conference on Data Science, Machine Learning and Artificial IntelligenceIn the world of medical imaging, detecting brain tumors is a difficult challenge. Manual segmentation of brain tumors from a vast number of MRI images for diagnosis was a complex and time-consuming task. Automatic brain tumor image segmentation serves a ...
Segmentation of pectoral muscle using the adaptive gamma corrections
Accurate segregation of pectoral muscles is very crucial in breast cancer detection. Pectoral segmentation is a challenging task due to heterogeneous tissues densities, neighborhood complexities and breast shape variabilities. This paper presents an ...
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain InjuriesAbstractStereotactic radiosurgery is a minimally-invasive treatment option for a large number of patients with intracranial tumors. As part of the therapy treatment, accurate delineation of brain tumors is of great importance. However, slice-by-slice ...
Comments