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Verma, Rachna
- Improve the Accuracy and Efficiency of Medical Diagnosis Analysis Using Knowledge Discovery
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Authors
Affiliations
1 Deptt. of Engineering (CSE), Dr. C. V. Raman University, Bilaspur (C.G), IN
2 Dr. C. V. Raman University, Bilaspur (C.G), IN
1 Deptt. of Engineering (CSE), Dr. C. V. Raman University, Bilaspur (C.G), IN
2 Dr. C. V. Raman University, Bilaspur (C.G), IN
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Data Mining and Knowledge Engineering, Vol 5, No 8 (2013), Pagination: 319-322Abstract
The main objective Computer-based support in health care is becoming ever more important. No other domain has so many innovative changes that have such a high social impact. There has already been a long standing tradition for computer-based decision support, dealing with complex problems in medicine such as diagnosing disease, managerial decisions and assisting in the prescription of appropriate treatment. The Healthcare industry is among the most information intensive industries. Medical information, knowledge and data keep growing on a daily basis. It has been estimated that an acute care hospital may generate five terabytes of data a year. The ability to use these data to extract useful information for quality healthcare is crucial. Computer assisted information retrieval may help support quality decision making and to avoid human error. Although human decision-making is often optimal, it is poor when there are huge amounts of data to be classified. Also efficiency and accuracy of decisions will decrease when humans are put into stress and immense work. Imagine a doctor who has to examine 5 patient records; he or she will go through them with ease. But if the number of records increases from 5 to 50 with a time constraint, it is almost certain that the accuracy with which the doctor delivers the decisions will not be as high as the ones obtained when he had only five records to be analyzed.Keywords
Health Care, Natural Language Processing, Fuzzy Logic, Classification, Intelligent Decision Support System, Identifying Patients, Diagnosis.
- Patch Based Stereo Matching Using Convolutional Neural Network
Abstract Views :226 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Jai Narain Vyas University, IN
2 Department of Production and Industrial Engineering, Jai Narain Vyas University, IN
1 Department of Computer Science and Engineering, Jai Narain Vyas University, IN
2 Department of Production and Industrial Engineering, Jai Narain Vyas University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 11, No 3 (2021), Pagination: 2366-2371Abstract
The paper presents a new Convolutional Neural Network (CNN) architecture, called stacked stereo CNN, for computing disparity map from stereo images. In stacked stereo CNN, left and right image patches are stacked back-to-back and fed to a single tower CNN. This is in contrast to Siamese network where two towers are used, one for the left patch and other for the right patch. The proposed network is trained on a large set of similar and dissimilar image patches, which are generated from stereo images and their ground truth images from Middlebury stereo datasets. The network returns a dissimilarity score for a pair of image patch which is used to compute the cost volume. The cost volume is further refined using post processing steps before generating the final disparity map. The proposed network is evaluated on Middlebury datasets and achieves comparable results to the state-of-art algorithms.Keywords
Stereo Vision, Patch Matching, Disparity Map, CNN.References
- K.Y. Kok and P. Rajendran, “A Review on Stereo Vision Algorithms: Challenges and Solutions”, ECTI Transactions on Computer and Information Technology, Vol. 13, No. 2, pp. 134-150, 2019.
- J. Zbontar and Y. Le Cuny, “Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches”, Journal of Machine Learning Research, Vol. 17, No. 2, pp. 1-32, 2016.
- S. Zagoruyko and N. Komodakis, “Learning to Compare Image Patches via Convolutional Neural Networks”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4353-4361, 2015.
- A. Tonioni, F. Tosi, M. Poggi, S. Mattoccia and L. di Stefano, “Real-Time Self-Adaptive Deep Stereo”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-13, 2019.
- X. Song, X. Zhao, L. Fang and H. Hu, “EdgeStereo: An Effective Multi-Task Learning Network for Stereo Matching and Edge Detection”, International Journal of Computer Vision, Vol. 128, pp. 910-930, 2020.
- Middlebury Stereo Datasets, Available at https://vision.middlebury.edu/stereo/data/ last, Accessed at 2020.
- R.A. Hamzah and H. Ibrahim, “Literature Survey on Stereo Vision Disparity Map Algorithms”, Journal of Sensors, Vol. 2016, pp. 1-23, 2016.
- K. Zhou, X. Meng and Bo Cheng, “Review of Stereo Matching Algorithms Based on Deep Learning”, Computational Intelligence and Neuroscience, Vol. 2020, pp. 1-18, 2020.
- W. Luo, A.G. Schwing and Raquel Urtasun, “Efficient Deep Learning for Stereo Matching”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5695-5703, 2016.
- H. Park and K.M. Lee, “Look Wider to Match Image Patches with Convolutional Neural Networks”, IEEE Signal Processing Letters, Vol. 24, pp. 1788-1792, 2017.
- X. Ye, J. Lib, H. Wang and X. Zhang, “Feature Ensemble Network with Occlusion Disambiguation for Accurate Patch-Based Stereo Matching”, IEICE Transactions on Information and Systems, Vol. 100, No. 12, pp. 3077-3080, 2017.
- P. Brandao, E. Mazomenos and D. Stoyanov, “Widening Siamese Architectures for Stereo Matching”, Pattern Recognition Letters, Vol. 120, pp. 75-81, 2019.
- B. Chen and C. Jung, “Patch-Based Stereo Matching using 3D Convolutional Neural Networks”, Proceedings of IEEE International Conference on Image Processing, pp. 3633-3637, 2018.
- H. Hirschmuller, “Stereo Processing by Semiglobal Matching and Mutual Information”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 2, pp. 328-341, 2008.
- R. Verma, H.S. Singh and A.K. Verma, “Depth Estimation from Stereo Images Based on Adaptive Weight and Segmentation”, Journal of the Institution of Engineers (India): Series B - Electrical, Electronics and Telecommunication and Computer Engineering, Vol. 93, No. 4, pp. 223-229, 2013.
- R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua and S. Süsstrunk, “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 11, pp. 2274-2282, 2012.