scholarly journals Diabetic Macular Edema Grading Based on Deep Neural Networks

2016 ◽  
Author(s):  
Baidaa Al-Bander ◽  
Waleed Al-Nuaimy ◽  
Majid A. Al-Taee ◽  
Bryan M. Williams ◽  
Yalin Zheng
Keyword(s):  
Neural Networks ◽  
Macular Edema ◽  
Diabetic Macular Edema ◽  
Deep Neural Networks

Automated segmentation of macular edema in OCT using deep neural networks

2019 ◽  
Vol 55 ◽  
pp. 216-227 ◽  
Author(s):  
Junjie Hu ◽  
Yuanyuan Chen ◽  
Zhang Yi
Keyword(s):  
Neural Networks ◽  
Macular Edema ◽  
Deep Neural Networks ◽  
Automated Segmentation

scholarly journals Hybrid CNN Based Computer-Aided Diagnosis System for Choroidal Neovascularization, Diabetic Macular Edema, Drusen Disease Detection from OCT Images

2021 ◽  
Vol 38 (3) ◽  
pp. 673-679
Author(s):  
Seda Arslan Tuncer ◽  
Ahmet Çınar ◽  
Murat Fırat
Keyword(s):  
Neural Networks ◽  
Macular Edema ◽  
Diabetic Macular Edema ◽  
Choroidal Neovascularization ◽  
Convolutional Neural Networks ◽  
Eye Diseases ◽  
Support Vector ◽  
Imaging Method ◽  
Cad System ◽  
Computer Aided

In the treatment of eye diseases, optical coherence tomography (OCT) is a medical imaging method that displays biological tissue layers by taking high resolution tomographic sections at the micron level. It has an important role in the diagnosis and follow-up of many diseases such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), age-related macular degeneration (AMD), Diabetic Retinopathy, Central Serous Retinopathy, Epiretinal Membrane, and Macular Hole. Computer-Aided Diagnostic (CAD) tools are needed in early detection and treatment monitoring of such eye diseases. In this paper, a hybrid Convolutional Neural Networks-based CAD system, which can classify Diabetic Macular Edema (DME), Drusen Choroidal Neovascularization (CNV), and normal OCT images, is proposed. The proposed system is CNN-SVM (Convolutional Neural Networks – Support Vector Machine) model and doesn’t require any additional extraction of feature or noise filtering on OCT images. A total of 968 OCT images is classified in pre-trained CNN methods with Alexnet, Resnet18 and Googlenet. Accuracy is achieved with highest Googlenet 97.4%. To examine the performance of the proposed CAD system, the CNN-SVM method achieves 98.96% with the highest accuracy hybrid Alexnet-SVM model, which is implemented with Alexnet-SVM, Resnet18-SVM and Googlenet-SVM models.

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks
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