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.

Classification of EgyptSat-1 Images Using Deep Learning Methods

2020 ◽  
Vol 10 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Hatem Keshk ◽  
Xu-Cheng Yin
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Performance ◽  
Satellite Images ◽  
Satellite Image ◽  
Aerial Photographs ◽  
Average Accuracy

Background: Deep Learning (DL) neural network methods have become a hotspot subject of research in the remote sensing field. Classification of aerial satellite images depends on spectral content, which is a challenging topic in remote sensing. Objective: With the aim to accomplish a high performance and accuracy of Egyptsat-1 satellite image classification, the use of the Convolutional Neural Network (CNN) is raised in this paper because CNN is considered a leading deep learning method. CNN is developed to classify aerial photographs into land cover classes such as urban, vegetation, desert, water bodies, soil, roads, etc. In our work, a comparison between MAXIMUM Likelihood (ML) which represents the traditional supervised classification methods and CNN method is conducted. Conclusion: This research finds that CNN outperforms ML by 9%. The convolutional neural network has better classification result, which reached 92.25% as its average accuracy. Also, the experiments showed that the convolutional neural network is the most satisfactory and effective classification method applied to classify Egyptsat-1 satellite images.

Diagnosis of Diabetic Retinopathy Using Machine Learning

2020 ◽  
pp. 477-481
Author(s):  
Balamurugan A ◽  
Vaisakhi V S ◽  
Surendran D ◽  
Umamaheswari S
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Vision Loss ◽  
Vision Impairment ◽  
Blurred Vision ◽  
Locus Model ◽  
Learning Technique ◽  
Classification Of Images

Diabetic retinopathy is an eye condition that can cause vision loss and blindness in people who have diabetics. It affects blood vessels in the retina. Initially, Diabetic retinopathy may not have any symptoms, but finding it early can help us to take steps to protect our vision. Some people notice changes in their vision, like trouble in reading or seeing faraway objects, these changes may come and go. In later stages of diseases, blood vessels in the retina starts to bleed into the vitreous. If this happens, you may see dark, floating spots or streaks that look like lobwels. Sometimes the spots clear up on their own, but it is important to start the treatment, otherwise it may get worse and the bleeding can happen again. There are various stages, it includes blurred vision, impairment of color vision, floaters, patches or streaks. Hence in our project, we came up with an idea of identifying diabetic retinopathy in early stages, to classify a given set of images into four classes, we are using supervised learning methods. For this task, we use deep learning technique with inception v3module along with skin locus model in order to achieve better results and for easy classification of images

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.

Detecting Diabetic Retinopathy Using Deep Learning Technique with Resnet-50

2021 ◽  
pp. 45-55
Author(s):  
Viraj Jiwane ◽  
Anubhav DattaGupta ◽  
Arunkumar Chauhan ◽  
Vidya Patil
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Technique

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.

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