scholarly journals Deep Feature Vectors Concatenation for Eye Disease Detection Using Fundus Image

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 23
Author(s):  
Radifa Paradisa ◽  
Alhadi Bustamam ◽  
Wibowo Mangunwardoyo ◽  
Andi Victor ◽  
Anggun Yudantha ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Proliferative Diabetic Retinopathy ◽  
Vision Loss ◽  
Diagnostic System ◽  
Diagnostic Process ◽  
Learning Approach ◽  
Fundus Image ◽  
Deep Feature ◽  
Fundus Photo

Fundus image is an image that captures the back of the eye (retina), which plays an important role in the detection of a disease, including diabetic retinopathy (DR). It is the most common complication in diabetics that remains an important cause of visual impairment, especially in the young and economically active age group. In patients with DR, early diagnosis can effectively help prevent the risk of vision loss. DR screening was performed by an ophthalmologist by analysing the lesions on the fundus image. However, the increasing prevalence of DR is not proportional to the availability of ophthalmologists who can read fundus images. It can lead to delayed prevention and management of DR. Therefore, there is a need for an automated diagnostic system as it can help ophthalmologists increase the efficiency of the diagnostic process. This paper provides a deep learning approach with the concatenate model for fundus image classification with three classes: no DR, non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR). The model architecture used is DenseNet121 and Inception-ResNetV2. The feature extraction results from the two models are combined and classified using the multilayer perceptron (MLP) method. The method that we propose gives an improvement compared to a single model with the results of accuracy, and average precision and recall of 91% and 90% for the F1-score, respectively. This experiment demonstrates that our proposed deep-learning approach is effective for the automatic DR classification using fundus photo data.

scholarly journals Deep Learning Approach for Automatic Microaneurysms Detection

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 542
Author(s):  
Muhammad Mateen ◽  
Tauqeer Safdar Malik ◽  
Shaukat Hayat ◽  
Musab Hameed ◽  
Song Sun ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Early Detection ◽  
Sensitivity And Specificity ◽  
Classification Accuracy ◽  
Vision Loss ◽  
State Of The Art ◽  
The State ◽  
Learning Approach ◽  
Manual Analysis

In diabetic retinopathy (DR), the early signs that may lead the eyesight towards complete vision loss are considered as microaneurysms (MAs). The shape of these MAs is almost circular, and they have a darkish color and are tiny in size, which means they may be missed by manual analysis of ophthalmologists. In this case, accurate early detection of microaneurysms is helpful to cure DR before non-reversible blindness. In the proposed method, early detection of MAs is performed using a hybrid feature embedding approach of pre-trained CNN models, named as VGG-19 and Inception-v3. The performance of the proposed approach was evaluated using publicly available datasets, namely “E-Ophtha” and “DIARETDB1”, and achieved 96% and 94% classification accuracy, respectively. Furthermore, the developed approach outperformed the state-of-the-art approaches in terms of sensitivity and specificity for microaneurysms detection.

scholarly journals Visual Cortex Transcranial Direct Current Stimulation for Proliferative Diabetic Retinopathy Patients: A Double-Blinded Randomized Exploratory Trial

2021 ◽  
Vol 11 (2) ◽  
pp. 270
Author(s):  
Angelito Braulio F. de Venecia ◽  
Shane M. Fresnoza
Keyword(s):  
Diabetic Retinopathy ◽  
Visual Cortex ◽  
Proliferative Diabetic Retinopathy ◽  
Direct Current ◽  
Transcranial Direct Current Stimulation ◽  
Vision Loss ◽  
Sham Stimulation ◽  
Direct Current Stimulation ◽  
Residual Vision ◽  
Cathodal Tdcs

Proliferative diabetic retinopathy (PDR) is a severe complication of diabetes. PDR-related retinal hemorrhages often lead to severe vision loss. The main goals of management are to prevent visual impairment progression and improve residual vision. We explored the potential of transcranial direct current stimulation (tDCS) to enhance residual vision. tDCS applied to the primary visual cortex (V1) may improve visual input processing from PDR patients’ retinas. Eleven PDR patients received cathodal tDCS stimulation of V1 (1 mA for 10 min), and another eleven patients received sham stimulation (1 mA for 30 s). Visual acuity (logarithm of the minimum angle of resolution (LogMAR) scores) and number acuity (reaction times (RTs) and accuracy rates (ARs)) were measured before and immediately after stimulation. The LogMAR scores and the RTs of patients who received cathodal tDCS decreased significantly after stimulation. Cathodal tDCS has no significant effect on ARs. There were no significant changes in the LogMAR scores, RTs, and ARs of PDR patients who received sham stimulation. The results are compatible with our proposal that neuronal noise aggravates impaired visual function in PDR. The therapeutic effect indicates the potential of tDCS as a safe and effective vision rehabilitation tool for PDR patients.

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 Wide-field imaging in proliferative diabetic retinopathy

2019 ◽  
Vol 5 (S1) ◽  
Author(s):  
T. Y. Alvin Liu ◽  
J. Fernando Arevalo
Keyword(s):  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Vision Loss ◽  
Current Data ◽  
Panretinal Photocoagulation ◽  
Main Body ◽  
Wide Field ◽  
Fundus Imaging ◽  
Field Imaging ◽  
Wide Field Imaging

Abstract Background Diabetic retinopathy (DR) is one of the leading causes of vision loss worldwide. For decades, 7-field 30-degree fundus imaging has been the gold standard for DR classification. The aim of this review article is to discuss how the advent of ultra-wide-field (UWF) fundus imaging has changed the management of proliferative diabetic retinopathy (PDR). Main body Current data suggests that UWF imaging, as compared to conventional Early Treatment Diabetic Retinopathy Study (ETDRS) fields, detects additional and more extensive PDR pathologies. DR lesions, captured by UWF imaging outside of ETDRS fields, likely carry prognostication value. Conclusion UWF imaging represents a major advancement in the detection and management of DR. It remains unclear whether, when and how patients, with PDR changes only peripheral to standard ETDRS fields, should be treated. A larger, prospective, randomized clinical trial is also needed to compare the efficacy of UWF image-guided targeted laser photocoagulation with that of conventional panretinal photocoagulation.

scholarly journals Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 274 ◽  
Author(s):  
Thippa Reddy Gadekallu ◽  
Neelu Khare ◽  
Sweta Bhattacharya ◽  
Saurabh Singh ◽  
Praveen Kumar Reddy Maddikunta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Early Detection ◽  
Dimensionality Reduction ◽  
Vision Loss ◽  
Principal Component ◽  
Screening Methods ◽  
Learning Approaches ◽  
Time Period

Diabetic Retinopathy is a major cause of vision loss and blindness affecting millions of people across the globe. Although there are established screening methods - fluorescein angiography and optical coherence tomography for detection of the disease but in majority of the cases, the patients remain ignorant and fail to undertake such tests at an appropriate time. The early detection of the disease plays an extremely important role in preventing vision loss which is the consequence of diabetes mellitus remaining untreated among patients for a prolonged time period. Various machine learning and deep learning approaches have been implemented on diabetic retinopathy dataset for classification and prediction of the disease but majority of them have neglected the aspect of data pre-processing and dimensionality reduction, leading to biased results. The dataset used in the present study is a diabetes retinopathy dataset collected from the UCI machine learning repository. At its inceptions, the raw dataset is normalized using the Standardscalar technique and then Principal Component Analysis (PCA) is used to extract the most significant features in the dataset. Further, Firefly algorithm is implemented for dimensionality reduction. This reduced dataset is fed into a Deep Neural Network Model for classification. The results generated from the model is evaluated against the prevalent machine learning models and the results justify the superiority of the proposed model in terms of Accuracy, Precision, Recall, Sensitivity and Specificity.

scholarly journals Deep Learning Approach for Stages of Severity Classification in Diabetic Retinopathy Using Color Fundus Retinal Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Silky Goel ◽  
Siddharth Gupta ◽  
Avnish Panwar ◽  
Sunil Kumar ◽  
Madhushi Verma ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Vision Loss ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Fundus Photography ◽  
Huge Amount ◽  
Early Screening ◽  
Image Metadata ◽  
Very High

Diabetes is a very fast-growing disease in India, with currently more than 72 million patients. Prolonged diabetes (about almost 20 years) can cause serious loss to the tiny blood vessels and neurons in the patient eyes, called diabetic retinopathy (DR). This first causes occlusion and then rapid vision loss. The symptoms of the disease are not very conspicuous in its early stage. The disease is featured by the formation of bloated structures in the retinal area called microaneurysms. Because of negligence, the condition of the eye worsens into the generation of more severe blots and damage to retinal vessels causing complete loss of vision. Early screening and monitoring of DR can reduce the risk of vision loss in patients with high possibilities. But the diabetic retinopathy detection and its classification by a human, is a challenging and error-prone task, because of the complexity of the image captured by color fundus photography. Machine learning algorithms armed with some feature extraction techniques have been employed earlier to detect and classify the levels of DR. However, these techniques provide below-par accuracy. Now, with the advent of deep learning (DL) techniques in computer vision, it has become possible to achieve very high levels of accuracy. DL models are an abstraction of the human brain coupled with the eyes. To create a model from scratch and train it is a cumbersome task requiring a huge amount of images. This deficiency of the DL techniques can be patched up by employing another technique to a task called transfer learning. In this, a DL model is trained on image metadata, and to learn features it used hundreds of classes from the DR fundus images. This enables professionals to create models capable of classifying unseen images into a proper grade or level with acceptable accuracy. This paper proposed a DL model coupled with different classifiers to classify the fundus image into its correct class of severity. We have trained the model on IDRD images and it has proven to show very high accuracy.

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