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Distribution of images across classes

Distribution of images across classes

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Article
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Diabetic retinopathy is a common complication of diabetes, that affects blood vessels in the light-sensitive tissue called the retina. It is the most common cause of vision loss among people with diabetes and the leading cause of vision impairment and blindness among working-age adults. Recent progress in the use of automated systems for diabetic r...

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... are five classes in data; from Table 1, one can see that classes are not uniformly represented. ...

Citations

... They achieved a validation precision of 86%, a recall of 87%, an f1-score of 86%, and a kappa score of 91.96%. Besides, Pak et al. [28] have drawn a comparison between two widely conventional architectures DenseNet and ResNet with the new optimized on EfficientNet. The proposed methods classify the retinal image of APTOS dataset into 5 classes. ...
Article
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Diabetic retinopathy is one of the most dangerous complications of diabetes. It affects the eyes causing damage to the blood vessels of the retina. Eventually, as the disease develops, it is possible to lose sight. The main cure for this pathology is based on the early detection which plays a crucial role in slowing the progress of the underlying disease and protecting many patients from losing their sight. However, the detection of diabetic retinopathy at its early stages remains an arduous task that requires human expert interpretation of fundus images in order to vigilantly follow-up the patient. In this paper, we shall propose a new automatic diabetic retinopathy detection method that based on deep-learning. The aforementioned approach is composed of two main steps: an initial pre-processing step where the deformable registration is applied on the retina to occupy the entire image and eliminate the effect of the background on the classification process. The second step is the classification phase in which we train four convolutional neural networks (CNN) models (Densenet-121, Xception, Inception-v3, Resnet-50) to detect the stage of diabetic retinopathy. The performance of our proposed architecture has been tested on the APTOS 2019 dataset. As the latter is relatively small, a transfer learning is adopted by pre-training the mentioned CNNs on the ImageNet dataset and fine-tuning them on the APTOS dataset. In the testing phase, the final prediction is obtained by a system of voting based on the output of the four convolutional neural networks. Our model has performed an accuracy of 85.28% in the testing phase.
... In our study, fundus images used in this study are publicly available from Kaggle 1 dataset. Images were provided by the Asia Pacific Tele-Ophthalmology Society (APTOS) as part of the 2019 Blindness Detection Competition [30,40]. The Kaggle dataset is one of the widely used and wellreported datasets for diabetic retinopathy. ...
Article
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During the recent years, diabetic retinopathy (DR) has been one of the most threatening complications of diabetes that leads to permanent blindness. Further, DR mutilates the retinal blood vessels of a patient having diabetes. Accordingly, various artificial intelligence techniques and deep learning have been proposed to automatically detect abnormalities in DR and its different stages from retina images. In this paper, we propose a hybrid deep learning approach using deep convolutional neural network (CNN) method and two VGG network models (VGG16 and VGG19) to diabetic retinopathy detection and classification according to the visual risk linked to the severity of retinal ischemia. Indeed, the classification of DR deals with understanding the images and their context with respect to the categories. The experimental results, performed on 5584 images, which are an ensemble of online datasets, yielded an accuracy of 90.60%, recall of 95% and F1 score of 94%. The main aim of this work is to develop a robust system for detecting and classifying DR automatically.
... The reasons for this would require further study but it may be conceptually important that the performance of the model depends on factors other than image resolution [15] or set size alone, with the network architecture possibly also an important factor contributing to model performance. The E cientNet [6] family of models has shown among other Convolutional Neural Networks e cacy in terms of performance and speed using commercially available GPU processing capabilities in the classi cation of skin lesions [16], CT lung scans [17] and diabetic retinopathy [18] but this is the probably one of the rst papers employing this model in paediatric elbow radiographs. In this study, a lower powered B1 version of the model was employed as compared to higher (i.e. ...
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Background To compare the performance of an AI model based on strategies designed to overcome small sized development sets to pediatric ER physicians at a classification triage task of pediatric elbow radiographs. Methods1,314 pediatric elbow lateral radiographs (mean age: 8.2 years) were retrospectively retrieved, binomially classified based on their annotation as normal or abnormal (with pathology), and randomly partitioned into a development set (993 images), tuning set (109 images), second tuning set (100 images) and test set (112 images). The AI model was trained on the development set and utilized the EfficientNet B1 compound scaling network architecture and online augmentations. Its performance on the test set was compared to a group of five physicians (inter-rater agreement: fair). Statistical analysis: AUC of AI model - DeLong method. Performance of AI model and physician groups - McNemar test. ResultsAccuracy of the model on the test set - 0.804 (95% CI, 0.718 - 0.873), AUROC - 0.872 (95% CI, 0.831 - 0.947). AI model performance compared to the physician group on the test set - sensitivity 0.790 (95% CI 0.684 to 0.895) vs 0.649 (95% CI 0.525 to 0.773), p value 0.088; specificity 0.818 (95% CI 0.716 to 0.920) vs 0.873 (95% CI 0.785 to 0.961), p value 0.439.Conclusions The AI model for elbow radiograph triage designed with strategies to optimize performance for a small sized development set showed comparable performance to physicians.
... Pak et al. [54] preprocessed the dataset and applied techniques such as data normalization and contrast adjustment. Then, they used out augmentation technique for training data in the dataset and used multiple CNN models in their study. ...
Article
Background and Objective : Diabetes-related cases can cause glaucoma, cataracts, optic neuritis, paralysis of the eye muscles, or various retinal damages over time. Diabetic retinopathy is the most common form of blindness that occurs with diabetes. Diabetic retinopathy is a disease that occurs when the blood vessels in the retina of the eye become damaged, leading to loss of vision in advanced stages. This disease can occur in any diabetic patient, and the most important factor in treating the disease is early diagnosis. Nowadays, deep learning models and machine learning methods, which are open to technological developments, are already used in early diagnosis systems. In this study, two publicly available datasets were used. The datasets consist of five types according to the severity of diabetic retinopathy. The objectives of the proposed approach in diabetic retinopathy detection are to positively contribute to the performance of CNN models by processing fundus images through preprocessing steps (morphological gradient and segmentation approaches). The other goal is to detect efficient sets from type-based activation sets obtained from CNN models using Atom Search Optimization method and increase the classification success. Methods : The proposed approach consists of three steps. In the first step, the Morphological Gradient method is used to prevent parasitism in each image, and the ocular vessels in fundus images are extracted using the segmentation method. In the second step, the datasets are trained with transfer learning models and the activations for each class type in the last fully connected layers of these models are extracted. In the last step, the Atom Search optimization method is used, and the most dominant activation class is selected from the extracted activations on a class basis. Results : When classified by the severity of diabetic retinopathy, an overall accuracy of 99.59% was achieved for dataset #1 and 99.81% for dataset #2. Conclusions : In this study, it was found that the overall accuracy achieved with the proposed approach increased. To achieve this increase, the application of preprocessing steps and the selection of the dominant activation sets from the deep learning models were implemented using the Atom Search optimization method.
... Pak et al. performed multiple classifications on the dataset samples with various deep learning architectures such as DenseNet121, ResNet50, ResNet101, and EfficientNet-b4. They achieved the highest accuracy using EfficientNet-b4 [35]. Gangwar and Ravi presented a Transfer Learning-based deep learning algorithm where they classified the images of this dataset with a pre-trained Inception-ResNet-v2 [36]. ...
Article
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Diabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of diabetes. DR has become a severe health concern worldwide, as the number of diabetes patients is soaring uncountably. Periodic eye examination allows doctors to detect DR in patients at an early stage to initiate proper treatments. Advancements in artificial intelligence and camera technology have allowed us to automate the diagnosis of DR, which can benefit millions of patients indeed. This paper inscribes a novel method for DR diagnosis based on the gray-level intensity and texture features extracted from fundus images using a decision tree-based ensemble learning technique. This study primarily works with the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset. We undertook several steps to curate its contents to make them more suitable for machine learning applications. Our approach incorporates several image processing techniques, two feature extraction techniques, and one feature selection technique, which results in a classification accuracy of 94.20% (margin of error: 0.32%) and an F-measure of 93.51% (margin of error: 0.5%). Several other parameters regarding the proposed method’s performance have been presented to manifest its robustness and reliability. Details on each employed technique have been included to make the provided results reproducible. This method can be a valuable tool for mass retinal screening to detect DR, thus drastically reducing the rate of vision loss attributed to it.
Article
Diabetic retinopathy (DR) is the most common cause of blindness in middle-aged people. It shows that an automatic image evaluation system is needed in the diagnosis of this disease due to the low number of scans. It is critical to meet this need that these systems are large-scale, cost-effective, and minimally invasive screening programs. With the use of deep learning techniques, it has become possible to develop these systems faster. In this study, a new approach based on feature selection with wrapper methods used for fundus images is presented that can be used for the classification of diabetic retinopathy. The fundus images used in the approach were improved with image processing techniques, thus eliminating unnecessary dark areas in the image. In this new approach, the most effective features are selected by wrapping methods over 512 deep features obtained from EfficientNet and DenseNet models. Binary Bat Algorithm (BBA), Equilibrium Optimizer (EO), Gravity Search Algorithm (GSA), and Gray Wolf Optimizer (GWO) were chosen as wrappers for the proposed approach. Selected features are classified by support vector machines and random forest machine learning methods. Considering the performance of this new approach, it gives the highest value of 96.32 accuracy and 0.98 kappa. These performance values were obtained with a minimum of 250 selected features. The Asia Pacific Tele-Ophthalmology Society (APTOS) dataset used to obtain these values was taken from a competition organized by Kaggle. The highest kappa value in this competition was reported as 0.93. This parameter clearly demonstrates the success of our approach.
Article
Diabetic retinopathy is one of the most threatening complications of diabetes that leads to permanent blindness if left untreated. Severity of the diabetic retinopathy disease is based on presence of microaneurysms, exudates, neovascularisation and haemorrhages. Convolutional neural networks have been successfully applied in many adjacent subjects, and for diagnosis of diabetic retinopathy itself. In this paper, an automatic deep-learning-based method for stage detection of diabetic retinopathy by single photography of the human fundus is proposed. Additionally, the multistage approach to transfer learning, which makes use of similar datasets with different labelling, is experimented. The proposed architecture gives high accuracy in classification through spatial analysis. Amongst other supervised algorithms involved, proposed solution is to find a better and optimized way to classifying the fundus image with little pre-processing techniques. The proposed architecture deployed with dropout layer techniques yields 78 percent accuracy.
Thesis
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Final publication: Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images (doi.org/10.3390/sym13040670)