Research Article |
CNN Classification of Multi-Scale Ensemble OCT for Macular Image Analysis
Author(s): P. Ananta Lakshmi1, G. Veerapandu2, Sridevi Gamini3 and Mahesh K. Singh4
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 4
Publisher : FOREX Publication
Published : 18 October 2022
e-ISSN : 2347-470X
Page(s) : 858-861
Abstract
Computer-Aided Diagnosis (CAD) of retinal pathology is a dynamic medical analysis area. The CAD system in the optical coherence tomography (OCT) is important for the monitoring of ocular diseases because of the heavy utilization of the retinal OCT imaging process. The Multi-Scale Expert Convolution Mixture (MCME) is designed to classify the normal retina. OCT is becoming one of the most popular non-invasive evaluation approaches for retinal eye disease. The amount of OCT is growing and the automation of OCT image analysis is becoming increasingly necessary. The surrogate-aided classification approach is to automatically classify retinal OCT images because of the Convolution Neural Network (CNN). The methods to classify OCT images and macular OCT classification are done by using CNN. Maculopathy is a combined collection of diseases to facilitate the effect of the inner region of the retina identified as the macula. Central Serous Choric Retinopathy (CSCR) and macular edema are the main two types of maculopathies. Numerous researches have focused on the detection of these macular disorders with OCT. It is used to overcome retinal diseases.
Keywords: CAD System
, CNN
, OCT
, Image analysis
, Macular Pathology
, Surrogate-assisted
.
P. Ananta Lakshmi, Department of ECE, Aditya College of Engineering, Surampalem, India; Email: anithaprathipati984@gmail.com
G. Veerapandu, Department of ECE, Aditya College of Engineering, Surampalem, India; Email: veerapandu_ece@acoe.edu.in
Sridevi Gamini, Department of ECE, Aditya Engineering College, Surampalem, India; Email: sridevi_gamini@yahoo.com
Mahesh K. Singh*, Department of ECE, Aditya Engineering College, Surampalem, India; Email: mahesh.singh@accendere.co.in
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[1] Rasti, Reza, HosseinRabbani, AlirezaMehridehnavi, and FedraHajizadeh. "Macular OCT classification using a multi-scale convolutional neural network ensemble." IEEE transactions on medical imaging 37, no. 4 (2017): 1024-1034.[Cross Ref]
-
[2] Rong, Yibiao, Dehui Xiang, Weifang Zhu, Kai Yu, Fei Shi, Zhun Fan, and Xinjian Chen. "Surrogate-assisted retinal OCT image classification based on convolutional neural networks." IEEE Journal of biomedical and health informatics 23, no. 1 (2018): 253-263.[Cross Ref]
-
[3] Wang, Depeng, and Liujen Wang. "On OCT image classification via deep learning."IEEE Photonics Journal 11, no. 5 (2019): 1-14.[Cross Ref]
-
[4] Hu, Zhihong, MeindertNiemeijer, Michael D. Abramoff, and Mona K. Garvin."Multimodal retinal vessel segmentation from spectral-domain optical coherence tomography and fundus photography." IEEE transactions on medical imaging 31, no. 10 (2012): 1900-1911.[Cross Ref]
-
[5] Mishra, Sapna S., BappadityaMandal, and N. B. Puhan. "Multi-level Dual-attention Based CNN for Macular Optical Coherence Tomography Classification." IEEE Signal Processing Letters 26, no. 12 (2019): 1793-1797.[Cross Ref]
-
[6] Xu, Xiayu, Kyungmoo Lee, Li Zhang, Milan Sonka, and Michael D. Abràmoff. "Stratified sampling voxel classification for segmentation of intraretinal and subretinal fluid in longitudinal clinical OCT data." IEEE transactions on medical imaging 34, no. 7 (2015): 1616-1623.[Cross Ref]
-
[7] Campos-Delgado, Daniel U., Omar Gutierrez-Navarro, Jose J. Rico-Jimenez, Elvis Duran-Sierra, HimarFabelo, Samuel Ortega, Gustavo Callicó, and Javier A. Jo. "Extended Blind End-Member and Abundance Extraction for Biomedical Imaging Applications." IEEE Access 7 (2019): 1.[Cross Ref]
-
[8] Singh, M., Nandan, D., & Kumar, S. (2019). Statistical Analysis of Lower and Raised Pitch Voice Signal and Its Efficiency Calculation. Traitement du Signal, 36(5), 455-461.[Cross Ref]
-
[9] Mouli, D. C., Kumar, G. V., Kiran, S. V., & Kumar, S. (2021, October). Video Retrieval Queries of Large-Scale Images: An Efficient Approach. In 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC) (pp. 247-250). IEEE.[Cross Ref]
-
[10] Swarnima Singh, Vikash Yadav (2021), An Improved Particle Swarm Optimization for Prediction of Accident Severity. IJEER 9(3), 42-47. DOI: 10.37391/IJEER.090304. [Cross Ref]
-
[11] Sudeep, S. V. N. V. S., Venkata Kiran, S., Nandan, D., & Kumar, S. (2021). An Overview of Biometrics and Face Spoofing Detection. ICCCE 2020, 871-881.[Cross Ref]
-
[12] Chen, M., Lang, A., Ying, H. S., Calabresi, P. A., Prince, J. L., & Carass, A. (2014). Analysis of macular OCT images using deformable registration. Biomedical optics express, 5(7), 2196-2214.A [Cross Ref]
-
[13] Huang, Y., Danis, R. P., Pak, J. W., Luo, S., White, J., Zhang, X., ... & Domalpally, A. (2013). Development of a semi-automatic segmentation method for retinal OCT images tested in patients with diabetic macular edema. PloS one, 8(12), e82922.[Cross Ref]
-
[14] Kiran, K. S., Preethi, V., & Kumar, S. (2022). A brief review of organic solar cells and materials involved in its fabrication. Materials Today: Proceedings.[Cross Ref]
-
[15] Garvin, M. K., Abràmoff, M. D., Kardon, R., Russell, S. R., Wu, X., & Sonka, M. (2008). Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search. IEEE transactions on medical imaging, 27(10), 1495-1505.[Cross Ref]
-
[16] M. I. Thariq Hussan, D. Saidulu, P. T. Anitha, A. Manikandan and P. Naresh (2022), Object Detection and Recognition in Real Time Using Deep Learning for Visually Impaired People. IJEER 10(2), 80-86. DOI: 10.37391/IJEER.100205. [Cross Ref]
-
[17] Santhoshi, M. S., Sharath Babu, K., Kumar, S., & Nandan, D. (2021). An investigation on rolling element bearing fault and real-time spectrum analysis by using short-time fourier transform. In Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications (pp. 561-567). Springer, Singapore.[Cross Ref]
-
[18] Gao, Z., Bu, W., Zheng, Y., & Wu, X. (2017). Automated layer segmentation of macular OCT images via graph-based SLIC superpixels and manifold ranking approach. Computerized Medical Imaging and Graphics, 55, 42-53.[Cross Ref]
-
[19] Roy, AbhijitGuha, SaileshConjeti, Stephane G. Carlier, Pranab K. Dutta, Adnan Kastrati, Andrew F. Laine, Nassir Navab, Amin Katouzian, and Debdoot Sheet. "Lumen segmentation in intravascular optical coherence tomography using backscattering tracked and initialized random walks." IEEE journal of biomedical and health informatics 20, no. 2 (2015): 606-614. 78539-178552.[Cross Ref]
-
[20] Hassan, Taimur, Muhammad UsmanAkram, ArslanShaukat, SajidGulKhawaja, and Bilal Hassan. "Structure Tensor Graph Searches Based Fully Automated Grading and 3D Profiling of Maculopathy From Retinal OCT Images." IEEE Access 6 (2018): 44644-44658.[Cross Ref]
-
[21] Quellec, Gwénolé, Kyungmoo Lee, Martin Dolejsi, Mona K. Garvin, Michael D. Abramoff, and Milan Sonka. "Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula." IEEE transactions on medical imaging 29, no. 6 (2010): 1321-1330.[Cross Ref]
P. Ananta Lakshmi, G. Veerapandu, Sridevi Gamini and Mahesh K. Singh (2022), CNN Classification of Multi-Scale Ensemble OCT for Macular Image Analysis. IJEER 10(4), 858-861. DOI: 10.37391/IJEER.100417.