Convolutional Neural Networks for Medical Image Diagnosis and Prognosis
One of the most incredible machine learning methods is deep learning. Utilised for picture categorization, clinical archiving, item identification, and other purposes. The quantity of medical image archives is expanding at an alarming rate as hospitals employ digital photos for documentation more frequently. Digital imaging is essential for assessing the severity of a patient's illness. Medical imaging has a wide variety of uses in research and diagnostics. Due to recent developments in image processing technology, self-operating identification of medical photos is still a research area for computer vision researchers. We require an appropriate classifier in order to categorise medical photos using various classifiers. After organ prediction and classification, the research was modified to include medical picture recognition. For medical picture detection, pretrained convolutional networks and Kmean clustering techniques similar to those used for organ identification are employed. Separating the training from the test data allowed for the data's authentication. The application of this strategy has been proven to be most effective for categorising various medical images of human organs.
Q. Zhu, B. Du, B. Turkbey, P. Choyke, & P. Yan, (2018). Exploiting interslice correlation for MRI prostate image segmentation, from recursive neural networks aspect. Complexity,2018. https://doi.org/10.1155/2018/4185279
Q. Li, W. Cai, X. Wang, Y. Zhou, D.D. Feng, M. Chen (2014). Medical image classification with convolutional neural network. 13th International Conference on Control Automation Robotics & Vision (ICARCV), IEEE, Singapore. https://doi.org/10.1109/ICARCV.2014.7064414
X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, R.M. Summers, Chestx-ray8: hospital-scale chest X-ray database andbenchmarks on weakly-supervised classification and localization of common thorax diseases. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, USA. https://doi.org/10.1109/CVPR.2017.369
V.P. Vianna, (2018). Study and development of a computer-aided diagnosis system for classification of chest X-ray images using convolutional neural networks pre-trained for imagenet and data augmentation. arXiv preprint.
Y. Xu, T. Mo, Q. Feng, P. Zhong, M. Lai, I. Eric, & C. Chang, (2014). Deep learning of feature representation with multiple instance learning for medical image analysis. IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, 1626-1630.
M. Reza Zare, A. Mueen, & W. Chaw Seng, Automatic classification of medical X‐ray images using a bag of visual words. IET Computer Vision, 7(2) (2013). 105-114. https://doi.org/10.1049/iet-cvi.2012.0291
D.S. Kermany et al., Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5) (2018) 1122-1131. https://doi.org/10.1016/j.cell.2018.02.010
R. Ashraf et al., Deep Convolution Neural Network for Big Data Medical Image Classification," in IEEE Access, 8 (2020) 105659-105670,.https://doi.org/10.1109/ACCESS.2020.2998808
J. Wan, D. Wang, S.C.H. Hoi, P. Wu, J. Zhu, Y. Zhang, & J. Li, Deep learning for content-based image retrieval: A comprehensive study. In Proceedings of the 22nd ACM international conference on Multimedia. (2014) 157-166. https://doi.org/10.1145/2647868.2654948
A. Krizhevsky, & I. Sutskever, Geoffrey E. hinton, ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing System 25 (2012).
S.H. Dar T. Mahmood, M. Yasir, Z. Abbas, Deep convolution neural network for big data medical image classification, IEEE Access, 8 (2020) 105659-105670. https://doi.org/10.1109/ACCESS.2020.2998808
A. Parameswari, K.V. Kumar, & S. Gopinath, Thermal analysis of Alzheimer’s disease prediction using random forest classification model, Materials Today: Proceedings. 66(3) (2022) 815-821. https://doi.org/10.1016/j.matpr.2022.04.357
Copyright (c) 2022 Parameswari A, Vinoth Kumar K
This work is licensed under a Creative Commons Attribution 4.0 International License.
Views: Abstract : 33 | PDF : 19