Identification of Diabetic Retinopathy Using Weighted Fusion Deep Learning Based on Dual-Channel Fundus Scans

Nneji, Grace Ugochi; Cai, Jingye; Deng, Jianhua; Monday, Happy Nkanta; Hossin, Md Altab; Nahar, Saifun

doi:10.3390/diagnostics12020540

Cited by 51 publications

(31 citation statements)

References 32 publications

(34 reference statements)

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“…A large amount of data often leads to higher accuracy 21 . Previous researchers have often used datasets from open-source web platforms to train models 22,23 . This study utilized the opensource dataset provided on Kaggle to obtain a large amount of image data and achieved an accuracy rate of 96% in test 1.…”

Section: Resultsmentioning

confidence: 99%

Deep learning for automatic diagnosis of fundus diseases on optical coherence tomography images

Yang

Wang

Huang

et al. 2022

Preprint

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Background There has been increasing attention on the use of deep learning systems and transfer learning to identify disease features and progression. In this study, we aimed to apply deep learning algorithms to Optical coherence tomography (OCT) images to quickly identify common referable fundus diseases. Method A total of 24000 OCT images (test 1) retrospectively acquired from the Kaggle database with age-related macular degeneration, choroidal neovascularization, central serous chorioretinopathy, diabetic macular edema, diabetic retinopathy, drusen, macular hole, and NOR were used to develop the model. Images were split into training, validation, and testing sets. The convolutional neural networks ResNet101 and DenseNet121 were trained to classify images. The area under the receiver operating characteristic curve (AUC), accuracy, and F1 score were calculated to evaluate the performance of the models. A total of 800 OCT images (test 2) diagnosed with the above eight diseases were collected from the Zhengzhou Eye Hospital to retest the accuracy of the models. Results ResNet101 performed better than DenseNet121 did. The classification performance in terms of accuracy and F1 score of ResNet101 were 0.9398 and 0.9360, respectively, in test 2. The AUC of ResNet101 for the eight diseases based on test 2 were 0.9956 (macro-average) and 0.9932 (micro-average). When using DenseNet121 in test 2, the accuracy was 0.7130, and the F1 score was 0.7116. The macro-average AUC was 0.8519, and the micro-average AUC was 0.8366. Conclusions Convolutional neural network ResNet101 and transfer learning showed good performance in discriminating between OCT images. As a promising adjunctive tool, our model can provide rapid provisional diagnosis for patients with common referable fundus diseases.

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Section: Resultsmentioning

confidence: 99%

Deep learning for automatic diagnosis of fundus diseases on optical coherence tomography images

Yang

Wang

Huang

et al. 2022

Preprint

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“…The authors of Ref. [24,25,29,34,36] implement Inception V3 while those of Ref. [33,40] used Inception V4.…”

Section: Convolutional Neural Network Architecture Modelsmentioning

confidence: 99%

“…Some of the more commonly used are stochastic gradient descent which is applied by Ref. [5, 34], Adaptive Moment Estimation Algorithm (ADAM) [33] and RMSprop [22]. Shankar [44] mentioned that the Bayesian optimization model can be used to tune a model as it analyses the previous validation outcome in which it utilises to create a probabilistic model, which will map the hyperparameters to a probability score.…”

Section: Existing Workmentioning

confidence: 99%

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Deep learning in the grading of diabetic retinopathy: A review

Tajudin

Kipli

Mahmood

et al. 2022

IET Computer Vision

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Diabetic Retinopathy (DR) grading into different stages of severity continues to remain a challenging issue due to the complexities of the disease. Diabetic Retinopathy grading classifies retinal images to five levels of severity ranging from 0 to 5, which represents No DR, Mild non-proliferative diabetic retinopathy (NPDR), Moderate NPDR, Severe NPDR, and proliferative diabetic retinopathy. With the advancement of Deep Learning, studies on the application of the Convolutional Neural Network (CNN) in DR grading have been on the rise. High accuracy and sensitivity are the desired outcome of these studies. This paper reviewed recently published studies that employed CNN for DR grading to 5 levels of severity. Various approaches are applied in classifying retinal images which are, (i) by training CNN models to learn the features for each grade and (ii) by detecting and segmenting lesions using information about their location such as microaneurysms, exudates, and haemorrhages. Public and private datasets have been utilised by researchers in classifying retinal images for DR. The performance of the CNN models was measured by accuracy, specificity, sensitivity, and area under the curve. The CNN models and their performance varies for every study. More research into the CNN model is necessary for future work to improve model performance in DR grading. The Inception model can be used as a starting point for subsequent research. It will also be necessary to investigate the attributes that the model uses for grading.

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“…Both RT-PCR tests and CT scans are relatively expensive [ 11 , 12 ] and quite a number of nations are mandated to conduct limited testing for only risk-prone populations due to excessive demand. CXR imaging is a comparably low-cost method of detecting lung infections, and it can also be used to detect COVID-19 [ 13 , 14 , 15 , 16 , 17 ]. As a result, automated algorithms that can accurately classify COVID-19 from CXR exams are extremely useful in the fight against the pandemic.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network

Monday

Nneji

et al. 2022

Healthcare

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Since it was first reported, coronavirus disease 2019, also known as COVID-19, has spread expeditiously around the globe. COVID-19 must be diagnosed as soon as possible in order to control the disease and provide proper care to patients. The chest X-ray (CXR) has been identified as a useful diagnostic tool, but the disease outbreak has put a lot of pressure on radiologists to read the scans, which could give rise to fatigue-related misdiagnosis. Automatic classification algorithms that are reliable can be extremely beneficial; however, they typically depend upon a large amount of COVID-19 data for training, which are troublesome to obtain in the nick of time. Therefore, we propose a novel method for the classification of COVID-19. Concretely, a novel neurowavelet capsule network is proposed for COVID-19 classification. To be more precise, first, we introduce a multi-resolution analysis of a discrete wavelet transform to filter noisy and inconsistent information from the CXR data in order to improve the feature extraction robustness of the network. Secondly, the discrete wavelet transform of the multi-resolution analysis also performs a sub-sampling operation in order to minimize the loss of spatial details, thereby enhancing the overall classification performance. We examined the proposed model on a public-sourced dataset of pneumonia-related illnesses, including COVID-19 confirmed cases and healthy CXR images. The proposed method achieves an accuracy of 99.6%, sensitivity of 99.2%, specificity of 99.1% and precision of 99.7%. Our approach achieves an up-to-date performance that is useful for COVID-19 screening according to the experimental results. This latest paradigm will contribute significantly in the battle against COVID-19 and other diseases.

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Identification of Diabetic Retinopathy Using Weighted Fusion Deep Learning Based on Dual-Channel Fundus Scans

Cited by 51 publications

References 32 publications

Deep learning for automatic diagnosis of fundus diseases on optical coherence tomography images

Deep learning for automatic diagnosis of fundus diseases on optical coherence tomography images

Deep learning in the grading of diabetic retinopathy: A review

COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network

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