Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning

2020 ◽  
Vol 258 (4) ◽  
pp. 779-785 ◽  
Author(s):  
Xiangji Pan ◽  
Kai Jin ◽  
Jing Cao ◽  
Zhifang Liu ◽  
Jian Wu ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Fluorescein Angiography ◽  
Automatic Analysis ◽  
Fundus Fluorescein Angiography ◽  
Retinal Lesions

scholarly journals Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

2020 ◽  
Vol 10 (6) ◽  
pp. 2021 ◽  
Author(s):  
Ibrahem Kandel ◽  
Mauro Castelli
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Transfer Learning ◽  
Main Idea ◽  
Diabetic Patients ◽  
Domain Transfer ◽  
Domain Transfer Learning ◽  
Enormous Number ◽  
Deep Learning Model

Diabetic retinopathy (DR) is a dangerous eye condition that affects diabetic patients. Without early detection, it can affect the retina and may eventually cause permanent blindness. The early diagnosis of DR is crucial for its treatment. However, the diagnosis of DR is a very difficult process that requires an experienced ophthalmologist. A breakthrough in the field of artificial intelligence called deep learning can help in giving the ophthalmologist a second opinion regarding the classification of the DR by using an autonomous classifier. To accurately train a deep learning model to classify DR, an enormous number of images is required, and this is an important limitation in the DR domain. Transfer learning is a technique that can help in overcoming the scarcity of images. The main idea that is exploited by transfer learning is that a deep learning architecture, previously trained on non-medical images, can be fine-tuned to suit the DR dataset. This paper reviews research papers that focus on DR classification by using transfer learning to present the best existing methods to address this problem. This review can help future researchers to find out existing transfer learning methods to address the DR classification task and to show their differences in terms of performance.

scholarly journals Differential distribution of manifest lesions in diabetic retinopathy by fundus fluorescein angiography and fundus photography

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Xiaoli Li ◽  
Jie Xie ◽  
Liang Zhang ◽  
Ying Cui ◽  
Guanrong Zhang ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Fluorescein Angiography ◽  
Posterior Pole ◽  
Chinese Patients ◽  
Fundus Photography ◽  
Peripheral Retina ◽  
Multiple Logistic Regression ◽  
Fundus Fluorescein Angiography ◽  
Differential Distribution ◽  
Nonproliferative Diabetic Retinopathy

Abstract Background To analyze the distribution of manifest lesions of diabetic retinopathy (DR) by fundus fluorescein angiography (FFA) and color fundus photography (FP). Methods A total of 566 eyes of 324 Chinese patients diagnosed with DR were included in this retrospective study. DR severity was graded by the international grading criterion. The distributions of microaneurysms (MA), intraretinal hemorrhages/exudates (He/Ex), intraretinal microvascular abnormality (IRMA), capillary nonperfusion areas (NPA), and neovascularization (NV) were estimated by multiple logistic regression analyse based on nine-field FFA and FP images. Results In mild nonproliferative diabetic retinopathy (NPDR), the highest frequency of MA was found in the posterior pole (67.7%), followed by the inferior nasal (59.4%), and the nasal (55.4%) fields. In moderate NPDR, MA frequently distributed in the posterior pole (98.0%), nasal (97.0%), superior (96.0%), inferior nasal (94.9%), and inferior (92.9%) fields, whereas He/Ex were most prevalent in the posterior pole (69.7%). In severe NPDR and proliferative DR, IRMA, NPA, and NV were more frequent in the nasal field, particularly in the inferior nasal field (60.3, 38.7, and 76.0%, respectively). All lesions were more observed in the combined posterior pole, nasal, and inferior nasal fields than in the posterior pole or combined two fields in the early and severe stages of DR (P < 0.05). Conclusions The manifest lesions of DR were common in the nasal field besides the posterior pole in Chinese patients. A combined examination of the posterior pole, nasal, and inferior nasal mid-peripheral retina would help to detect different retinal lesions of DR. Trial registration ClinicalTrial. gov, NCT03528720. Registered 18 May 2018 - Retrospectively registered.

Detection and classification of retinal lesions for grading of diabetic retinopathy

2014 ◽  
Vol 45 ◽  
pp. 161-171 ◽  
Author(s):  
M. Usman Akram ◽  
Shehzad Khalid ◽  
Anam Tariq ◽  
Shoab A. Khan ◽  
Farooque Azam
Keyword(s):  
Diabetic Retinopathy ◽  
Retinal Lesions

scholarly journals Literature Survey on Diabetic Retinopathy Classification Using Deep Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 2259-2262
Author(s):  
Alfiya Md. Shaikh
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Transfer Learning ◽  
Medical Condition ◽  
Literature Survey ◽  
Fundus Images ◽  
Fundus Image ◽  
Automatic Diagnosis ◽  
Global Blindness

Abstract: Diabetic retinopathy (DR) is a medical condition that damages eye retinal tissues. Diabetic retinopathy leads to mild to complete blindness. It has been a leading cause of global blindness. The identification and categorization of DR take place through the segmentation of parts of the fundus image or the examination of the fundus image for the incidence of exudates, lesions, microaneurysms, and so on. This research aims to study and summarize various recent proposed techniques applied to automate the process of classification of diabetic retinopathy. In the current study, the researchers focused on the concept of classifying the DR fundus images based on their severity level. Emphasis is on studying papers that proposed models developed using transfer learning. Thus, it becomes vital to develop an automatic diagnosis system to support physicians in their work.

scholarly journals Classification of Stages Diabetic Retinopathy Using MobileNetV2 Model

10.29007/h46n ◽  
2022 ◽  
Author(s):  
Hoang Nhut Huynh ◽  
Minh Thanh Do ◽  
Gia Thinh Huynh ◽  
Anh Tu Tran ◽  
Trung Nghia Tran
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
High Performance ◽  
Vision Loss ◽  
Training Model ◽  
Average Accuracy ◽  
Common Diagnosis ◽  
Learning Technique ◽  
The Common

Diabetic retinopathy (DR) is a complication of diabetes mellitus that causes retinal damage that can lead to vision loss if not detected and treated promptly. The common diagnosis stages of the disease take time, effort, and cost and can be misdiagnosed. In the recent period with the explosion of artificial intelligence, deep learning has become the most popular tool with high performance in many fields, especially in the analysis and classification of medical images. The Convolutional Neural Network (CNN) is more widely used as a deep learning method in medical imaging analysis with highly effective. In this paper, the five-stage image of modern DR (healthy, mild, moderate, severe, and proliferative) can be detected and classified using the deep learning technique. After cross-validation training and testing on the corresponding 5,590-image dataset, a pre-MobileNetV2 training model is proposed in classifying stages of diabetic retinopathy. The average accuracy of the model achieved was 93.89% with the precision of 94.00%, recall 92.00% and f1-score 90.00%. The corresponding thermal image is also given to help experts for evaluating the influence of the retina in each different stage.

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