Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification

Bodapati, Jyostna Devi; Shaik, Nagur Shareef; Naralasetti, Veeranjaneyulu

doi:10.1007/s12652-020-02727-z

Cited by 87 publications

(19 citation statements)

References 33 publications

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“…Our CNN512 on the DDR dataset achieved a 0.886 ACC, while in the works of [ 32 , 37 ] achieved 0.828 and 0.856 ACC, respectively. In the APTOS 2019 dataset, our CNN512 achieved a 0.841 ACC, which is better than the works of [ 31 , 33 , 34 ]. The results of the CNNs in both datasets are shown in Table 10 and Table 11 , respectively.…”

Section: Experiments and Resultsmentioning

confidence: 74%

“…Kassani et al [ 33 ] modified Xception model [ 43 ] to classify the DR stages in the APTOS 2019 Kaggle dataset [ 46 ], resulting in a 83.09% ACC. Bodapati et al [ 34 ] proposed a composite network with gated attention to classify DR images into stages, achieving an ACC of 82.54% in the APTOS 2019 Kaggle dataset [ 46 ]. Hsieh et al [ 35 ] trained the modified Inception-v4 [ 19 ] and the modified ResNet [ 21 ] to detect any DR, proliferative DR and referable DR in their private dataset and the EYEPACS dataset.…”

Section: Related Workmentioning

confidence: 99%

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Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning

Alyoubi

Abulkhair

Shalash

2021

Sensors

136

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Diabetic retinopathy (DR) is a disease resulting from diabetes complications, causing non-reversible damage to retina blood vessels. DR is a leading cause of blindness if not detected early. The currently available DR treatments are limited to stopping or delaying the deterioration of sight, highlighting the importance of regular scanning using high-efficiency computer-based systems to diagnose cases early. The current work presented fully automatic diagnosis systems that exceed manual techniques to avoid misdiagnosis, reducing time, effort and cost. The proposed system classifies DR images into five stages—no-DR, mild, moderate, severe and proliferative DR—as well as localizing the affected lesions on retain surface. The system comprises two deep learning-based models. The first model (CNN512) used the whole image as an input to the CNN model to classify it into one of the five DR stages. It achieved an accuracy of 88.6% and 84.1% on the DDR and the APTOS Kaggle 2019 public datasets, respectively, compared to the state-of-the-art results. Simultaneously, the second model used an adopted YOLOv3 model to detect and localize the DR lesions, achieving a 0.216 mAP in lesion localization on the DDR dataset, which improves the current state-of-the-art results. Finally, both of the proposed structures, CNN512 and YOLOv3, were fused to classify DR images and localize DR lesions, obtaining an accuracy of 89% with 89% sensitivity, 97.3 specificity and that exceeds the current state-of-the-art results.

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Section: Experiments and Resultsmentioning

confidence: 74%

Section: Related Workmentioning

confidence: 99%

Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning

Alyoubi

Abulkhair

Shalash

2021

Sensors

136

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“…When trained on RA-EfficientNet, we observe that the total number of correctly predicted images is 722, demonstrating the advantages of our diagnostic model. To better understand the representative of our RA-EfficientNet, we compare it with existing methods in the literature [11,14]. In Table 5, we can see that the proposed method achieves 98.36% accuracy, which performs significantly better than existing methods.…”

Section: Task 1: 2-grade Classificationmentioning

confidence: 99%

“…Dondeti et al [13], based on the APTOS 2019 dataset, combined the pre-training model NASNET with the T-SNE space to extract deep features, achieving an accuracy rate of 77.90%. Bodapati et al [14] proposed a composite deep neural network of Xception and VGG16 with gated attention mechanism to automatically diagnose DR. The accuracy of this model on the APTOS 2019 dataset was 82.54%.…”

Section: Introductionmentioning

confidence: 99%

Diabetic Retinopathy Diagnosis Based on RA-EfficientNet

Yang

Wang

et al. 2021

Applied Sciences

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The early detection and grade diagnosis of diabetic retinopathy (DR) are very important for the avoidance of blindness, and using deep learning methods to automatically diagnose DR has attracted great attention. However, the small amount of DR data limits its application. To automatically learn the disease’s features and detect DR more accurately, we constructed a DR grade diagnostic model. To realize the model, the authors performed the following steps: firstly, we preprocess the DR images to solve the existing problems in an APTOS 2019 dataset, such as size difference, information redundancy and the data imbalance. Secondly, to extract more valid image features, a new network named RA-EfficientNet is proposed, in which a residual attention (RA) block is added to EfficientNet to extract more features and to solve the problem of small differences between lesions. EfficientNet has been previously trained on the ImageNet dataset, based on transfer learning technology, to overcome the small sample size problem of DR. Lastly, based on the extracted features, two classifiers are designed, one is a 2-grade classifier and the other a 5-grade classifier. The 2-grade classifier can diagnose DR, and the 5-grade classifier provides 5 grades of diagnosis for DR, as follows: 0 for No DR, 1 for mild DR, 2 for moderate, 3 for severe and 4 for proliferative DR. Experiments show that our proposed RA-EfficientNet can achieve better performance, with an accuracy value of 98.36% and a kappa score of 96.72% in a 2-grade classification and an accuracy value of 93.55% and a kappa score of 91.93% in a 5-grade classification. The results indicate that the proposed model effectively improves DR detection efficiency and resolves the existing limitation of manual feature extraction.

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“…Recent advances in the Computer Vision Field (CVF) have grabbed the attention of the research community [ 10 , 11 , 58 ] and they started exploring ways to apply these models for medical image analysis applications such as tumor classification [ 8 , 9 , 53 ], retinopathy identification [ 6 , 7 , 52 ] and even for COVID-19 diagnosis [ 34 ]. This section of the manuscript summarizes recent works proposed in the literature for COVID-19 detection from X-ray and CT scan images.…”

Section: Background Studymentioning

confidence: 99%

Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans

Shaik

Cherukuri

2022

Computers in Biology and Medicine

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Coronavirus Disease 2019 (COVID-19) is a deadly infection that affects the respiratory organs in humans as well as animals. By 2020, this disease turned out to be a pandemic affecting millions of individuals across the globe. Conducting rapid tests for a large number of suspects preventing the spread of the virus has become a challenge. In the recent past, several deep learning based approaches have been developed for automating the process of detecting COVID-19 infection from Lung Computerized Tomography (CT) scan images. However, most of them rely on a single model prediction for the final decision which may or may not be accurate. In this paper, we propose a novel ensemble approach that aggregates the strength of multiple deep neural network architectures before arriving at the final decision. We use various pre-trained models such as VGG16, VGG19, InceptionV3, ResNet50, ResNet50V2, InceptionResNetV2, Xception, and MobileNet and fine-tune them using Lung CT Scan images. All these trained models are further used to create a strong ensemble classifier that makes the final prediction. Our experiments exhibit that the proposed ensemble approach is superior to existing ensemble approaches and set state-of-the-art results for detecting COVID-19 infection from lung CT scan images.

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Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification

Cited by 87 publications

References 33 publications

Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning

Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning

Diabetic Retinopathy Diagnosis Based on RA-EfficientNet

Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans

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