Recent Applications of Deep Learning Algorithms in Medical Image Analysis
Keywords:
Deep learning, Convolutional Neural Network, Image Classification, Medical ScienceAbstract
Advances in deep learning have enabled researchers in the field of medical imaging to employ such techniques for various applications, including early diagnosis of different diseases. Deep learning techniques such as convolutional neural networks offer the capability of extracting invariant features from images that can improve the performance of most predictive models in medical and diagnostic imaging. This work concentrates on reviewing deep learning architectures along with medical imaging modalities where the crucial applications of such algorithms, including image classification and segmentation, are discussed. Also, brain imaging as a branch of medical imaging which allows scientists to explore the structure and function of the brain is explored, and the applications of deep learning to early diagnose Alzheimer’s Disease, and Autism as the most critical brain disorders are studied. Moreover, the recent research findings revealed that employing deep learning-based semantic segmentation techniques could significantly improve the accuracy of models developed for brain tumor detection. Such advances in early diagnosis of disorders and tumors encourage medical imaging practitioners to implement software applications assisting them to improve their decision-making process.
References
. Sarraf, S., Binary Image Segmentation Using Classification Methods: Support Vector Machines, Artificial Neural Networks and K th Nearest Neighbours. International Journal of Computer (IJC), 2017. 24(1): p. 56-79.
. Lawrence, S., et al., Face recognition: A convolutional neural-network approach. IEEE transactions on neural networks, 1997. 8(1): p. 98-113.
. Sarraf, S., Hair color classification in face recognition using machine learning algorithms. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 2016. 26(3): p. 317-334.
. Wang, X., A. Shrivastava, and A. Gupta. A-fast-rcnn: Hard positive generation via adversary for object detection. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
. Sarraf, S., French Word Recognition through a Quick Survey on Recurrent Neural Networks Using Long-Short Term Memory RNN-LSTM. arXiv preprint arXiv:1804.03683, 2018.
. Sarraf, S., EEG-based movement imagery classification using machine learning techniques and Welch’s power spectral density estimation. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 2017. 33(1): p. 124-145.
. Sarraf, S. and J. Sun, Advances in functional brain imaging: a comprehensive survey for engineers and physical scientists. International Journal of Advanced Research, 2016. 4(8): p. 640-660.
. Strother, S.C., S. Sarraf, and C. Grady. A hierarchy of cognitive brain networks revealed by multivariate performance metrics. in 2014 48th Asilomar Conference on Signals, Systems and Computers. 2014. IEEE.
. Sarraf, S., et al., Resting-State fMRI Data Classification of Exercise-Induced Brain Changes in Healthy Subjects Using Probabilistic Independent Component Analysis (PICA). 2013, Researchgate.
. Saverino, C., et al., The associative memory deficit in aging is related to reduced selectivity of brain activity during encoding. Journal of cognitive neuroscience, 2016. 28(9): p. 1331-1344.
. Lateef, F. and Y. Ruichek, Survey on semantic segmentation using deep learning techniques. Neurocomputing, 2019. 338: p. 321-348.
. Garcia-Garcia, A., et al., A review on deep learning techniques applied to semantic segmentation. arXiv preprint arXiv:1704.06857, 2017.
. Zhao, B., et al., A survey on deep learning-based fine-grained object classification and semantic segmentation. International Journal of Automation and Computing, 2017. 14(2): p. 119-135.
. Anderson, J.A., et al., Task-linked diurnal brain network reorganization in older adults: A graph theoretical approach. Journal of Cognitive Neuroscience, 2017. 29(3): p. 560-572.
. Liu, Z., et al., Towards clinical diagnosis: Automated stroke lesion segmentation on multi-spectral MR image using convolutional neural network. IEEE Access, 2018. 6: p. 57006-57016.
. Zhong, Z., et al. 3D fully convolutional networks for co-segmentation of tumors on PET-CT images. in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). 2018. IEEE.
. Grady, C., et al., Age differences in the functional interactions among the default, frontoparietal control, and dorsal attention networks. Neurobiology of aging, 2016. 41: p. 159-172.
. Machulda, M.M., et al., Comparison of memory fMRI response among normal, MCI, and Alzheimer’s patients. Neurology, 2003. 61(4): p. 500-506.
. Yang, X., S. Sarraf, and N. Zhang, Deep learning-based framework for Autism functional MRI image classification. Journal of the Arkansas Academy of Science, 2018. 72(1): p. 47-52.
. Sarraf, S. and G. Tofighi. Deep learning-based pipeline to recognize Alzheimer’s disease using fMRI data. in 2016 Future Technologies Conference (FTC). 2016. IEEE.
. Sarraf, S. and M. Ostadhashem. Big data application in functional magnetic resonance imaging using apache spark. in 2016 Future Technologies Conference (FTC). 2016. IEEE.
. Sarraf, S., et al., DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI. bioRxiv, 2017: p. 070441.
. Sarraf, S., C. Saverino, and A.M. Golestani. A robust and adaptive decision-making algorithm for detecting brain networks using functional mri within the spatial and frequency domain. in 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). 2016. IEEE.
. Sarraf, S., et al., MCADNNet: Recognizing stages of cognitive impairment through efficient convolutional fMRI and MRI neural network topology models. IEEE Access, 2019. 7: p. 155584-155600.
. LeCun, Y., et al., Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998. 86(11): p. 2278-2324.
Downloads
Published
How to Cite
Issue
Section
License
Authors who submit papers with this journal agree to the following terms.
