AOCT-NET: a Convolutional Network Automated Classification of Multiclass Retinal Diseases Using Spectral-domain Optical Coherence Tomography Images

Overview

Journal Med Biol Eng Comput

Publisher Springer

Specialties Biomedical Engineering
Medical Informatics

Date 2019 Nov 16

PMID 31728935

Citations 33

Authors

Ali Mohammad Alqudah

Affiliations

Soon will be listed here.

Abstract

Since introducing optical coherence tomography (OCT) technology for 2D eye imaging, it has become one of the most important and widely used imaging modalities for the noninvasive assessment of retinal eye diseases. Age-related macular degeneration (AMD) and diabetic macular edema eye disease are the leading causes of blindness being diagnosed using OCT. Recently, by developing machine learning and deep learning techniques, the classification of eye retina diseases using OCT images has become quite a challenge. In this paper, a novel automated convolutional neural network (CNN) architecture for a multiclass classification system based on spectral-domain optical coherence tomography (SD-OCT) has been proposed. The system used to classify five types of retinal diseases (age-related macular degeneration (AMD), choroidal neovascularization (CNV), diabetic macular edema (DME), and drusen) in addition to normal cases. The proposed CNN architecture with a softmax classifier overall correctly identified 100% of cases with AMD, 98.86% of cases with CNV, 99.17% cases with DME, 98.97% cases with drusen, and 99.15% cases of normal with an overall accuracy of 95.30%. This architecture is a potentially impactful tool for the diagnosis of retinal diseases using SD-OCT images.

Citing Articles

A Low Complexity Efficient Deep Learning Model for Automated Retinal Disease Diagnosis.

Chowa S, Bhuiyan M, Payel I, Karim A, Khan I, Montaha S J Healthc Inform Res. 2025; 9(1):1-40.

PMID: 39897099 PMC: 11782765. DOI: 10.1007/s41666-024-00182-5.

Session-by-Session Prediction of Anti-Endothelial Growth Factor Injection Needs in Neovascular Age-Related Macular Degeneration Using Optical-Coherence-Tomography-Derived Features and Machine Learning.

Ragni F, Bovo S, Zen A, Sona D, De Nadai K, Adamo G Diagnostics (Basel). 2024; 14(23).

PMID: 39682518 PMC: 11639924. DOI: 10.3390/diagnostics14232609.

AI-based 3D analysis of retinal vasculature associated with retinal diseases using OCT angiography.

Liu Y, Tang Z, Li C, Zhang Z, Zhang Y, Wang X Biomed Opt Express. 2024; 15(11):6416-6432.

PMID: 39553857 PMC: 11563331. DOI: 10.1364/BOE.534703.

Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence.

Heinke A, Zhang H, Broniarek K, Michalska-Malecka K, Elsner W, Galang C Sci Rep. 2024; 14(1):27085.

PMID: 39511248 PMC: 11544254. DOI: 10.1038/s41598-024-78327-0.

Multi-resolution visual Mamba with multi-directional selective mechanism for retinal disease detection.

Zuo Q, Shi Z, Liu B, Ping N, Wang J, Cheng X Front Cell Dev Biol. 2024; 12:1484880.

PMID: 39463765 PMC: 11512455. DOI: 10.3389/fcell.2024.1484880.

References

Hinton G, Osindero S, Teh Y . A fast learning algorithm for deep belief nets. Neural Comput. 2006; 18(7):1527-54. DOI: 10.1162/neco.2006.18.7.1527. View

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

Lemaitre G, Rastgoo M, Massich J, Cheung C, Wong T, Lamoureux E . Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection. J Ophthalmol. 2016; 2016:3298606. PMC: 4983398. DOI: 10.1155/2016/3298606. View

Pierro L, Zampedri E, Milani P, Gagliardi M, Isola V, Pece A . Spectral domain OCT versus time domain OCT in the evaluation of macular features related to wet age-related macular degeneration. Clin Ophthalmol. 2012; 6:219-23. PMC: 3280103. DOI: 10.2147/OPTH.S27656. View

Lee C, Baughman D, Lee A . Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration. Ophthalmol Retina. 2019; 1(4):322-327. PMC: 6347658. DOI: 10.1016/j.oret.2016.12.009. View

Karri S, Chakraborty D, Chatterjee J . Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. Biomed Opt Express. 2017; 8(2):579-592. PMC: 5330546. DOI: 10.1364/BOE.8.000579. View

Alsaih K, Lemaitre G, Rastgoo M, Massich J, Sidibe D, Meriaudeau F . Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images. Biomed Eng Online. 2017; 16(1):68. PMC: 5463338. DOI: 10.1186/s12938-017-0352-9. View

Schmidt-Erfurth U, Sadeghipour A, Gerendas B, Waldstein S, Bogunovic H . Artificial intelligence in retina. Prog Retin Eye Res. 2018; 67:1-29. DOI: 10.1016/j.preteyeres.2018.07.004. View

Ting D, Pasquale L, Peng L, Campbell J, Lee A, Raman R . Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2018; 103(2):167-175. PMC: 6362807. DOI: 10.1136/bjophthalmol-2018-313173. View

10.

Hussain M, Bhuiyan A, Luu C, Smith R, Guymer R, Ishikawa H . Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm. PLoS One. 2018; 13(6):e0198281. PMC: 5986153. DOI: 10.1371/journal.pone.0198281. View

11.

Kanagasingam Y, Bhuiyan A, Abramoff M, Smith R, Goldschmidt L, Wong T . Progress on retinal image analysis for age related macular degeneration. Prog Retin Eye Res. 2013; 38:20-42. DOI: 10.1016/j.preteyeres.2013.10.002. View

12.

Hwang D, Hsu C, Chang K, Chao D, Sun C, Jheng Y . Artificial intelligence-based decision-making for age-related macular degeneration. Theranostics. 2019; 9(1):232-245. PMC: 6332801. DOI: 10.7150/thno.28447. View

13.

Rong Y, Xiang D, Zhu W, Yu K, Shi F, Fan Z . Surrogate-Assisted Retinal OCT Image Classification Based on Convolutional Neural Networks. IEEE J Biomed Health Inform. 2018; 23(1):253-263. DOI: 10.1109/JBHI.2018.2795545. View

14.

Srinivasan P, Kim L, Mettu P, Cousins S, Comer G, Izatt J . Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed Opt Express. 2014; 5(10):3568-77. PMC: 4206325. DOI: 10.1364/BOE.5.003568. View

15.

Farsiu S, Chiu S, OConnell R, Folgar F, Yuan E, Izatt J . Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. Ophthalmology. 2013; 121(1):162-172. PMC: 3901571. DOI: 10.1016/j.ophtha.2013.07.013. View

16.

Gao F, Yue Z, Wang J, Sun J, Yang E, Zhou H . A Novel Active Semisupervised Convolutional Neural Network Algorithm for SAR Image Recognition. Comput Intell Neurosci. 2017; 2017:3105053. PMC: 5651152. DOI: 10.1155/2017/3105053. View

17.

Sajda P . Machine learning for detection and diagnosis of disease. Annu Rev Biomed Eng. 2006; 8:537-65. DOI: 10.1146/annurev.bioeng.8.061505.095802. View

18.

Amil P, Gonzalez L, Arrondo E, Salinas C, Guell J, Masoller C . Unsupervised feature extraction of anterior chamber OCT images for ordering and classification. Sci Rep. 2019; 9(1):1157. PMC: 6362085. DOI: 10.1038/s41598-018-38136-8. View

19.

Rasti R, Rabbani H, Mehridehnavi A, Hajizadeh F . Macular OCT Classification Using a Multi-Scale Convolutional Neural Network Ensemble. IEEE Trans Med Imaging. 2018; 37(4):1024-1034. DOI: 10.1109/TMI.2017.2780115. View

20.

Reis A, OLeary N, Yang H, Sharpe G, Nicolela M, Burgoyne C . Influence of clinically invisible, but optical coherence tomography detected, optic disc margin anatomy on neuroretinal rim evaluation. Invest Ophthalmol Vis Sci. 2012; 53(4):1852-60. PMC: 3995560. DOI: 10.1167/iovs.11-9309. View