Cellular Neural Network based Algorithm in Image Analysis of Age Related Macular Degeneration

2012 ◽  
Author(s):  
A.R. Chowdhury ◽  
S. Banerjee
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Macular Degeneration ◽  
Cellular Neural Network ◽  
Age Related Macular Degeneration ◽  
Age Related

scholarly journals Association of imaging factors derived from convolutional neural network with visual outcomes in age-related macular degeneration and polypoidal choroidal vasculopathy

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hyungwoo Lee ◽  
Minsu Jang ◽  
Hyung Chan Kim ◽  
Hyewon Chung
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Macular Degeneration ◽  
Polypoidal Choroidal Vasculopathy ◽  
Age Related Macular Degeneration ◽  
Final Visit ◽  
Visual Gain ◽  
Visual Outcomes ◽  
Age Related ◽  
Choroidal Vasculopathy

AbstractWe investigated the association of visual outcome in typical neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) with or without pachychoroid with lesion areas on optical coherence tomography (OCT) quantified by convolutional neural network (CNN) analysis. Treatment-naïve 132 nAMD and 45 PCV eyes treated with ranibizumab or aflibercept for at least 12 months were retrospectively reviewed. Significant factors, including intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED) and subretinal hyperreflective material (SHRM) area quantified by CNN at baseline and 12 months, were analyzed by logistic regression analyses for 3-line visual gain or maintenance of 20/30 Snellen vision. Visual gain at the final visit in nAMD was associated with a smaller SHRM at baseline (OR 0.167, P = 0.03), greater decrease in SRF and SHRM from baseline to month 12 (OR 1.564, P = 0.02; OR 12.877, P = 0.01, respectively). Visual gain in nAMD without pachychoroid was associated with a greater decrease in SRF and SHRM (OR 1.574, P = 0.03, OR 1.775, P = 0.04). No association was found in nAMD with pachychoroid and any type of PCV. Greater decrease in SRF and SHRM from baseline to month 12 was associated with favorable visual outcomes in nAMD without pachychoroid but not in nAMD with pachychoroid and PCV.

scholarly journals Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study

2020 ◽  
Vol 2020 ◽  
pp. 1-7 ◽  
Author(s):  
Ehsan Vaghefi ◽  
Sophie Hill ◽  
Hannah M. Kersten ◽  
David Squirrell
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Fundus Photography ◽  
Multiple Modalities ◽  
Age Related ◽  
Dry Amd ◽  
Fundus Photographs

Background and Objective. To determine if using a multi-input deep learning approach in the image analysis of optical coherence tomography (OCT), OCT angiography (OCT-A), and colour fundus photographs increases the accuracy of a CNN to diagnose intermediate dry age-related macular degeneration (AMD). Patients and Methods. Seventy-five participants were recruited and divided into three cohorts: young healthy (YH), old healthy (OH), and patients with intermediate dry AMD. Colour fundus photography, OCT, and OCT-A scans were performed. The convolutional neural network (CNN) was trained on multiple image modalities at the same time. Results. The CNN trained using OCT alone showed a diagnostic accuracy of 94%, whilst the OCT-A trained CNN resulted in an accuracy of 91%. When multiple modalities were combined, the CNN accuracy increased to 96% in the AMD cohort. Conclusions. Here we demonstrate that superior diagnostic accuracy can be achieved when deep learning is combined with multimodal image analysis.

scholarly journals Classification of age-related macular degeneration using convolutional-neural-network-based transfer learning

2021 ◽  
Vol 22 (S5) ◽  
Author(s):  
Yao-Mei Chen ◽  
Wei-Tai Huang ◽  
Wen-Hsien Ho ◽  
Jinn-Tsong Tsai
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Macular Degeneration ◽  
Transfer Learning ◽  
Age Related Macular Degeneration ◽  
Batch Size ◽  
Learning Capability ◽  
Age Related ◽  
Starting Point ◽  
And Performance

Abstract Background To diagnose key pathologies of age-related macular degeneration (AMD) and diabetic macular edema (DME) quickly and accurately, researchers attempted to develop effective artificial intelligence methods by using medical images. Results A convolutional neural network (CNN) with transfer learning capability is proposed and appropriate hyperparameters are selected for classifying optical coherence tomography (OCT) images of AMD and DME. To perform transfer learning, a pre-trained CNN model is used as the starting point for a new CNN model for solving related problems. The hyperparameters (parameters that have set values before the learning process begins) in this study were algorithm hyperparameters that affect learning speed and quality. During training, different CNN-based models require different algorithm hyperparameters (e.g., optimizer, learning rate, and mini-batch size). Experiments showed that, after transfer learning, the CNN models (8-layer Alexnet, 22-layer Googlenet, 16-layer VGG, 19-layer VGG, 18-layer Resnet, 50-layer Resnet, and a 101-layer Resnet) successfully classified OCT images of AMD and DME. Conclusions The experimental results further showed that, after transfer learning, the VGG19, Resnet101, and Resnet50 models with appropriate algorithm hyperparameters had excellent capability and performance in classifying OCT images of AMD and DME.

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