scholarly journals Image-based Glaucoma Classification Using Fundus Images and Deep Learning

2021 ◽  
Author(s):  
◽  
Hiram José Sandoval-Cuellar
Keyword(s):  
Deep Learning ◽  
Vision Loss ◽  
Rapid Development ◽  
Area Under The Curve ◽  
Region Of Interest ◽  
Fundus Images ◽  
Non Invasive ◽  
Glaucoma Detection ◽  
Initial Stage ◽  
Interest Detection

Glaucoma is an eye disease that gradually affects the optic nerve. Intravascular high pressure can be controlled to prevent total vision loss, but early glaucoma detection is crucial. The optic disc has been a notable landmark for finding abnormalities in the retina. The rapid development of computer vision techniques has made it possible to analyze eye conditions from images enabling to help a specialist to make a diagnosis using a technique that is non-invasive in its initial stage through fundus images. We propose a methodology glaucoma detection using deep learning. A convolutional neural network (CNN) is trained to extract multiple features, to classify fundus images. The accuracy, sensitivity, and the area under the curve obtained using the ORIGA database are 93.22%, 94.14%, and 93.98%. The use of the algorithm for the automatic region of interest detection in conjunction with our CNN structure considerably increases the glaucoma detecting accuracy in the ORIGA database.

scholarly journals Segmenting the Optic Disc Using a Deep Learning Ensemble Model Based on OWA Operators

2021 ◽  
Author(s):  
Mohammed Yousef Salem Ali ◽  
Mohamed Abdel-Nasser ◽  
Mohammed Jabreel ◽  
Aida Valls ◽  
Marc Baget
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Vision Loss ◽  
Semantic Segmentation ◽  
Medical Image Segmentation ◽  
Weighted Averaging ◽  
Fundus Images ◽  
Owa Operators ◽  
Detection Systems ◽  
Glaucoma Detection

The optic disc (OD) is the point where the retinal vessels begin. OD carries essential information linked to Diabetic Retinopathy and glaucoma that may cause vision loss. Therefore, accurate segmentation of the optic disc from eye fundus images is essential to develop efficient automated DR and glaucoma detection systems. This paper presents a deep learning-based system for OD segmentation based on an ensemble of efficient semantic segmentation models for medical image segmentation. The aggregation of the different DL models was performed with the ordered weighted averaging (OWA) operators. We proposed the use of a dynamically generated set of weights that can give a different contribution to the models according to their performance during the segmentation of OD in the eye fundus images. The effectiveness of the proposed system was assessed on a fundus image dataset collected from the Hospital Sant Joan de Reus. We obtained Jaccard, Dice, Precision, and Recall scores of 95.40, 95.10, 96.70, and 93.90%, respectively.

scholarly journals Deep learning on fundus images detects glaucoma beyond the optic disc

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruben Hemelings ◽  
Bart Elen ◽  
João Barbosa-Breda ◽  
Matthew B. Blaschko ◽  
Patrick De Boever ◽  
...  
Keyword(s):  
Deep Learning ◽  
Area Under The Curve ◽  
Coefficient Of Determination ◽  
Fundus Images ◽  
Fundus Image ◽  
Image Size ◽  
Learning Models ◽  
Glaucoma Detection ◽  
Significant Performance ◽  
Estimation Models

AbstractAlthough unprecedented sensitivity and specificity values are reported, recent glaucoma detection deep learning models lack in decision transparency. Here, we propose a methodology that advances explainable deep learning in the field of glaucoma detection and vertical cup-disc ratio (VCDR), an important risk factor. We trained and evaluated deep learning models using fundus images that underwent a certain cropping policy. We defined the crop radius as a percentage of image size, centered on the optic nerve head (ONH), with an equidistant spaced range from 10–60% (ONH crop policy). The inverse of the cropping mask was also applied (periphery crop policy). Trained models using original images resulted in an area under the curve (AUC) of 0.94 [95% CI 0.92–0.96] for glaucoma detection, and a coefficient of determination (R2) equal to 77% [95% CI 0.77–0.79] for VCDR estimation. Models that were trained on images with absence of the ONH are still able to obtain significant performance (0.88 [95% CI 0.85–0.90] AUC for glaucoma detection and 37% [95% CI 0.35–0.40] R2 score for VCDR estimation in the most extreme setup of 60% ONH crop). Our findings provide the first irrefutable evidence that deep learning can detect glaucoma from fundus image regions outside the ONH.

An Explainable Deep Learning Ensemble Model for Robust Diagnosis of Diabetic Retinopathy Grading

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-24
Author(s):  
Mohammad Shorfuzzaman ◽  
M. Shamim Hossain ◽  
Abdulmotaleb El Saddik
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Vision Loss ◽  
Final Diagnosis ◽  
Learning Rate ◽  
Ensemble Model ◽  
Fundus Images ◽  
Robust Detection ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetic retinopathy (DR) is one of the most common causes of vision loss in people who have diabetes for a prolonged period. Convolutional neural networks (CNNs) have become increasingly popular for computer-aided DR diagnosis using retinal fundus images. While these CNNs are highly reliable, their lack of sufficient explainability prevents them from being widely used in medical practice. In this article, we propose a novel explainable deep learning ensemble model where weights from different models are fused into a single model to extract salient features from various retinal lesions found on fundus images. The extracted features are then fed to a custom classifier for the final diagnosis of DR severity level. The model is trained on an APTOS dataset containing retinal fundus images of various DR grades using a cyclical learning rates strategy with an automatic learning rate finder for decaying the learning rate to improve model accuracy. We develop an explainability approach by leveraging gradient-weighted class activation mapping and shapely adaptive explanations to highlight the areas of fundus images that are most indicative of different DR stages. This allows ophthalmologists to view our model's decision in a way that they can understand. Evaluation results using three different datasets (APTOS, MESSIDOR, IDRiD) show the effectiveness of our model, achieving superior classification rates with a high degree of precision (0.970), sensitivity (0.980), and AUC (0.978). We believe that the proposed model, which jointly offers state-of-the-art diagnosis performance and explainability, will address the black-box nature of deep CNN models in robust detection of DR grading.

scholarly journals Hyperglycemia Identification Using ECG in Deep Learning Era

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6263
Author(s):  
Renato Cordeiro ◽  
Nima Karimian ◽  
Younghee Park
Keyword(s):  
Deep Learning ◽  
Blood Glucose Concentration ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Physiological Signals ◽  
Ecg Signals ◽  
Non Invasive ◽  
Learning Classifier ◽  
Electrocardiogram Ecg ◽  
Smart Wearable

A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the performance of the deep learning classifier. We evaluate the proposed method with ECG data from 1119 different subjects to assess the efficiency of hyperglycemia detection of the proposed work. The result indicates that the proposed algorithm is effective in detecting hyperglycemia with a 94.53% area under the curve (AUC), 87.57% sensitivity, and 85.04% specificity. That performance represents an relative improvement of 53% versus the best model found in the literature. The high sensitivity and specificity achieved by the 10-layer deep neural network proposed in this work provide an excellent indication that ECG possesses intrinsic information that can indicate the level of blood glucose concentration.

scholarly journals Ten Fast Transfer Learning Models for Carotid Ultrasound Plaque Tissue Characterization in Augmentation Framework Embedded with Heatmaps for Stroke Risk Stratification

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2109
Author(s):  
Skandha S. Sanagala ◽  
Andrew Nicolaides ◽  
Suneet K. Gupta ◽  
Vijaya K. Koppula ◽  
Luca Saba ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Tissue Characterization ◽  
Area Under The Curve ◽  
Learning Models ◽  
Non Invasive ◽  
Lower Accuracy ◽  
Improved Performance ◽  
Higher Order Spectra

Background and Purpose: Only 1–2% of the internal carotid artery asymptomatic plaques are unstable as a result of >80% stenosis. Thus, unnecessary efforts can be saved if these plaques can be characterized and classified into symptomatic and asymptomatic using non-invasive B-mode ultrasound. Earlier plaque tissue characterization (PTC) methods were machine learning (ML)-based, which used hand-crafted features that yielded lower accuracy and unreliability. The proposed study shows the role of transfer learning (TL)-based deep learning models for PTC. Methods: As pertained weights were used in the supercomputer framework, we hypothesize that transfer learning (TL) provides improved performance compared with deep learning. We applied 11 kinds of artificial intelligence (AI) models, 10 of them were augmented and optimized using TL approaches—a class of Atheromatic™ 2.0 TL (AtheroPoint™, Roseville, CA, USA) that consisted of (i–ii) Visual Geometric Group-16, 19 (VGG16, 19); (iii) Inception V3 (IV3); (iv–v) DenseNet121, 169; (vi) XceptionNet; (vii) ResNet50; (viii) MobileNet; (ix) AlexNet; (x) SqueezeNet; and one DL-based (xi) SuriNet-derived from UNet. We benchmark 11 AI models against our earlier deep convolutional neural network (DCNN) model. Results: The best performing TL was MobileNet, with accuracy and area-under-the-curve (AUC) pairs of 96.10 ± 3% and 0.961 (p < 0.0001), respectively. In DL, DCNN was comparable to SuriNet, with an accuracy of 95.66% and 92.7 ± 5.66%, and an AUC of 0.956 (p < 0.0001) and 0.927 (p < 0.0001), respectively. We validated the performance of the AI architectures with established biomarkers such as greyscale median (GSM), fractal dimension (FD), higher-order spectra (HOS), and visual heatmaps. We benchmarked against previously developed Atheromatic™ 1.0 ML and showed an improvement of 12.9%. Conclusions: TL is a powerful AI tool for PTC into symptomatic and asymptomatic plaques.

scholarly journals In Search for the Optimal Preprocessing Technique for Deep Learning Based Diabetic Retinopathy Stage Classification from Fundus Images

2021 ◽  
Author(s):  
Nilarun Mukherjee ◽  
Souvik Sengupta
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Vision Loss ◽  
Fundus Images ◽  
New Combinations ◽  
Z Score ◽  
Kappa Score ◽  
Stage Classification ◽  
Classification Tasks ◽  
Intensity Normalization

Abstract Background: Diabetic retinopathy (DR) is a complication of diabetes mellitus, which if left untreated may lead to complete vision loss. Early diagnosis and treatment is the key to prevent further complications of DR. Computer-aided diagnosis is a very effective method to support ophthalmologists, as manual inspection of pathological changes in retina images are time consuming and expensive. In recent times, Machine Learning and Deep Learning techniques have subsided conventional rule based approaches for detection, segmentation and classification of DR stages and lesions in fundus images. Method: In this paper, we present a comparative study of the different state-of-the-art preprocessing methods that have been used in deep learning based DR classification tasks in recent times and also propose a new unsupervised learning based retinal region extraction technique and new combinations of preprocessing pipelines designed on top of it. Efficacy of different existing and new combinations of the preprocessing methods are analyzed using two publicly available retinal datasets (EyePACS and APTOS) for different DR stage classification tasks, such as referable DR, DR screening, and five-class DR grading, using a benchmark deep learning model (ResNet-50). Results: It has been observed that the proposed preprocessing strategy composed of region of interest extraction through K-means clustering followed by contrast and edge enhancement using Graham’s method and z-score intensity normalization achieved the highest accuracy of 98.5%, 96.51% and 90.59% in DR-screening, referable-DR, and DR gradation tasks respectively and also achieved the best quadratic weighted kappa score of 0.945 in DR grading task. It achieved best AUC-ROC of 0.98 and 0.9981 in DR grading and DR screening tasks respectively. Conclusion: It is evident from the results that the proposed preprocessing pipeline composed of the proposed ROI extraction through K-means clustering, followed by edge and contrast enhancement using Graham’s method and then z-score intensity normalization outperforms all other existing preprocessing pipelines and has proven to be the most effective preprocessing strategy in helping the baseline CNN model to extract meaningful deep features.

scholarly journals Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kai Jin ◽  
Xiangji Pan ◽  
Kun You ◽  
Jian Wu ◽  
Zhifang Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescein Angiography ◽  
Macular Edema ◽  
Diabetic Macular Edema ◽  
Vision Loss ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Automatic Detection ◽  
Detection Algorithm ◽  
Fundus Fluorescein Angiography

Abstract Vision loss caused by diabetic macular edema (DME) can be prevented by early detection and laser photocoagulation. As there is no comprehensive detection technique to recognize NPA, we proposed an automatic detection method of NPA on fundus fluorescein angiography (FFA) in DME. The study included 3,014 FFA images of 221 patients with DME. We use 3 convolutional neural networks (CNNs), including DenseNet, ResNet50, and VGG16, to identify non-perfusion regions (NP), microaneurysms, and leakages in FFA images. The NPA was segmented using attention U-net. To validate its performance, we applied our detection algorithm on 249 FFA images in which the NPA areas were manually delineated by 3 ophthalmologists. For DR lesion classification, area under the curve is 0.8855 for NP regions, 0.9782 for microaneurysms, and 0.9765 for leakage classifier. The average precision of NP region overlap ratio is 0.643. NP regions of DME in FFA images are identified based a new automated deep learning algorithm. This study is an in-depth study from computer-aided diagnosis to treatment, and will be the theoretical basis for the application of intelligent guided laser.

scholarly journals Detection of Choroidalneovascularization (Cnv) in Retina Oct Images Using Vgg16 and Densenet Cnn

2021 ◽  
Author(s):  
Abirami M.S ◽  
Vennila B ◽  
Suganthi K ◽  
Sarthak Kawatra ◽  
Anuja Vaishnava
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Choroidal Neovascularization ◽  
Region Of Interest ◽  
Optical Coherence ◽  
Interest Detection ◽  
Learning Architectures

Abstract In this study, we intend to diagnose Choroidal Neovascularization in retinal Optical Coherence Tomography (OCT) images using Deep Learning Architectures. Optical Coherence Tomography (OCT) images can be used to differentiate between a healthy eye and an eye with CNV disease. DenseNet and Vgg16 Architectures of Deep Learning were used in the study and the hyper parameters of both of the architectures were changed to diagnose the disease properly. After the detection of the disease, the diseased OCT images are segmented from the background for the Region of Interest detection using Python OpenCV library that is used for the processing of images. The results of implementation of the Architectures showed that Vgg16 showed better results in detecting the images rather than Dense Net Architecture with an accuracy percentage of 97.53% approximately a percent greater than Dense Net.

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