AI, ML and the Eye Disease Detection

International Journal of Computer Science and Engineering

© 2020 by SSRG - IJCSE Journal

Volume 7 Issue 4

Year of Publication : 2020

Authors : Tian Jipeng, Suma P., Dr. T.C.Manjunath

10.14445/23488387/IJCSE-V7I4P101

How to Cite?

Tian Jipeng, Suma P., Dr. T.C.Manjunath, "AI, ML and the Eye Disease Detection," SSRG International Journal of Computer Science and Engineering , vol. 7,  no. 4, pp. 1-3, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I4P101

Abstract:

In this paper, a brief introduction to AI, ML and the Eye w.r.t. Deep Learning for Glaucoma Detection and Hardware Implementation is being presented. The result is the outcome of the Post-Graduate project work of the student that is going to be carried out in the second year of the course & this work is just the synopsis that is being framed for the carrying out of the detection of glaucoma disease.

Keywords:

Glaucoma, AI, ML, Data Analytics, Eye

References:

[1] Ali Serener , Sertan Serte, “Transfer Learning for Early and Advanced Glaucoma Detection with Convolutional Neural Networks”, Medical Technologies National Conference (TIPTEKNO) 2019.
[2] WeiLu, Yan Tong, Yue Yu, Yiqiao Xing, Changzheng Chen, and Yin Shen, “Applications of Artificial Intelligence in Ophthalmology: General Overview,” Hindawi, Journal of Ophthalmology , Volume 2018.
[3] Nooshin Mojab , Vahid Noroozi ,Philip S, Joelle A. Hallak, “Deep Multi-task Learning for Interpretable Glaucoma Detection,” 20th international conference on Reuse and Integration of Data Science, 2019.
[4] Mijung Kim, Jong Chul Han, Seung Hyup Hyun, Olivier Janssens, “Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning,” Article in Applied Science, 2019.
[5] H. Muhammad, T. J. Fuchs, C. N. De et al., “Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects,” Journal of Glaucoma, vol. 26, no. 12, pp. 1086–1094, 2017.
[6] R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology, vol. 123, no. 9, pp. 1974–1980, 2016.
[7] Li, J. Cheng, D. W. Wong et al., “Integrating holistic and local deep features for glaucoma classification,” in Proceedings of 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC),p.1328, Orlando, FL, USA, August 2016.
[8] X. Chen, Y. Xu, D. W. K. Wong, T. Y. Wong, and J. Liu, “Glaucoma detection based on deep convolutional neural network,” in Proceedings of 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milano, Italy, August 2015.
[9] Dinial Utami Nurul Qomariah, Handayani Tjandrasa, and Chastine Fatichah, “Classification of Diabetic Retinopathy and Normal Retinal Images using CNN and SVM,” 12th International Conference on Information & Communication Technology and System (lCTS) 2019.