References
- H. Müller, N. Michaux, D. Bandon, and A. Geissbuhler, “A review of content-based image retrieval systems in medical applications, Clinical benefits and future directions,” Int. J. Med. Inform., vol. 73, no. 1, pp. 1–23, 2004. doi: https://doi.org/10.1016/j.ijmedinf.2003.11.024
- K. N. Manjunath, A. Renuka, and U. C. Niranjan, “Linear models of cumulative distribution function for content-based medical image retrieval,” J. Med. Syst., vol. 31, pp. 433–443, Dec. 2007. doi: https://doi.org/10.1007/s10916-007-9075-y
- Dureja Aman, Pahwa Payal: Image Retrieval Techniques: A survey, International Journal of Engineering & Technology, Vol 7, No 1.2, 2018, pp. 215-219.
- Y. Gong, S. Lazebnik, A. Gordo, and F. Perronnin, “Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 12, pp. 2916–2929, 2013. doi: https://doi.org/10.1109/TPAMI.2012.193
- X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” IEEE Trans. Image Process., vol. 19, no. 6, pp. 1635–1650, Jun. 2010. doi: https://doi.org/10.1109/TIP.2010.2042645
- R. Gupta, H. Patil, and A. Mittal, “Robust order-based methods for feature description,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition., Jun. 2010, pp. 334–341.
- J. Lu, V. E. Liong, and J. Zhou, “Deep hashing for scalable image search,” IEEE Trans. Image Process., vol. 26, no. 5, pp. 2352–2367, May 2017. doi: https://doi.org/10.1109/TIP.2017.2678163
- R. Xia, Y. Pan, H. Lai, C. Liu, and S. Yan, “Supervised hashing for image retrieval via image representation learning,” in Proc. Nat. Conf. Artif. Intell., vol. 3, 2014, PP. 2156–2163.
- X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” J. Mach. Learn. Res., vol. 9, pp. 249–256, Jan. 2010.
- H. Lai, Y. Pan, Y. Liu, and S. Yan. Simultaneous feature learning and hash coding with deep neural networks. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
- Paras Lakhani, Baskaran Sundaram, “Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks”, Radiology, vol. 284, no. 2, 2017. doi: https://doi.org/10.1148/radiol.2017162326
- Pulkit Kumar, Monika Grewal, Muktabh Mayank Srivastava, “Boosted cascaded convents for multilabel classification of thoracic diseases in chest radiographs”, International Conference Image Analysis and Recognition, pp. 546-552, 2018. doi: https://doi.org/10.1007/978-3-319-93000-8_62
- Dev Kumar Das, Madhumala Ghosh, Mallika Pal, Asok K Maiti, Chandan Chakraborty, “Machine learning approach for automated screening of malaria parasite using light microscopic images”, Micron, vol. 45, 2013. doi: https://doi.org/10.1016/j.micron.2012.11.002
- T. I. Mohammad, A. A. Md, T. M. Ahmed, and A. Khalid, “Abnormality detection and localization in chest x-rays using deep convolutional neural networks,” 2017, http://arxiv.org/abs/1705.09850.
- Awais, M., Müller, H., Tang, T. B., & Meriaudeau, F. (2017, September). Classification of sd-oct images using a deep learning approach. In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 489-492). IEEE.
- Rong, Y., Xiang, D., Zhu, W., Yu, K., Shi, F., Fan, Z., & Chen, X. (2018). Surrogate-assisted retinal OCT image classification based on convolutional neural networks. IEEE journal of biomedical and health informatics, 23(1), 253-263. doi: https://doi.org/10.1109/JBHI.2018.2795545
- Pekala, M., Joshi, N., Liu, T. A., Bressler, N. M., DeBuc, D. C., & Burlina, P. (2019). Deep learning-based retinal OCT segmentation. Computers in biology and medicine, 114, 103445. doi: https://doi.org/10.1016/j.compbiomed.2019.103445
- Wang, D., & Wang, L. (2019). On OCT image classification via deep learning. IEEE Photonics Journal, 11(5), 1-14.
- He, X., Deng, Y., Fang, L., & Peng, Q. (2021). Multi-Modal Retinal Image Classification with Modality-Specific Attention Network. IEEE transactions on medical imaging, 40(6), 1591–1602. https://doi.org/https://doi.org/10.1109/TMI.2021.3059956.
- A. Gionis, P. Indyk, R. Motwani, et al., “Similarity search in high dimensions via hashing”, in VLDB, vol. 99, 1999, pp. 518–529.
- Y. Weiss, A. Torralba, and R. Fergus, “Spectral hashing”, in Advances in neural information processing systems, 2009, pp. 1753–1760.
- Daniel S. Kermany, Michael Goldbaum et. al., Identifying medical diagnoses and treatable diseases by Image-Based Deep Learning, Volume 172, Cell, DOI: https://doi.org/10.1016/j.cell.2018.02.010.
- J. Ren, X. Jiang, J. Yuan, and G. Wang, “Optimizing LBP structure for visual recognition using binary quadratic programming,” IEEE Signal Process. Lett., vol. 21, no. 11, pp. 1346–1350, Nov. 2014. doi: https://doi.org/10.1109/LSP.2014.2336252
- K. Lin, H.-F. Yang, J.-H. Hsiao, and C.-S. Chen, “Deep learning of binary hash codes for fast image retrieval”, in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2015, pp. 27–35.
- Islam, M. T., Aowal, M. A., Minhaz, A. T., & Ashraf, K. (2017). Abnormality detection and localization in chest x-rays using deep convolutional neural networks. arXiv preprint arXiv:1705.09850.
- Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016). “6.2.2.3 Softmax Units for Multinoulli Output Distributions”. Deep Learning. MIT Press. pp. 180–184. ISBN.
- https://www.image-net.org/download.php
- Deepika Kumar & Usha Batra (2020) An ensemble algorithm for breast cancer histopathology image classification, Journal of Statistics and Management Systems, 23:7, 1187–1198, doi: https://doi.org/10.1080/09720510.2020.1818451
- Aman Dureja & Payal Pahwa (2020) Medical image retrieval for detecting pneumonia using binary classification with deep convolutional neural networks, Journal of Information and Optimization Sciences, 41:6, 1419-1431, DOI: https://doi.org/10.1080/02522667.2020.1809096
- H. Swapnarekha, Himansu Sekhar Behera, Janmenjoy Nayak & Bighnaraj Naik (2021) Covid CT-net: A deep learning framework for COVID-19 prognosis using CT images, Journal of Interdisciplinary Mathematics, 24:2, 327-352, DOI: https://doi.org/10.1080/09720502.2020.1857905.