Deep convolution feature aggregation: an application to diabetic retinopathy severity level prediction

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

doi:10.1007/s11760-020-01816-y

Cited by 46 publications

(21 citation statements)

References 28 publications

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“…Bodapati et.al [36] 97.41 Chaturvedi et al [37] 96.51 Bodapati et al [38] 84.31 Proposed 97.93 The reported results show that our method achieved better performance than the comparative approaches, in terms of accuracy. It can be seen that our method achieved an average accuracy of 97.93% on the APTOS-2019 dataset and 98.10% on the IDRiD dataset, which is higher than other methods.…”

Section: Methods Accuracy (%)mentioning

confidence: 81%

“…We compared our method results with other techniques on both datasets, namely, APTOS-2019 and IDRiD. For the APTOS-2019 database, we considered the reported results of the comparative techniques, i.e., Bodapati et al [ 36 ], Chaturvedi et al [ 37 ] and Bodapati et al [ 38 ], and the results are reported in Table 4 . While, for the IDRiD dataset, we compared our results with the approaches of Wu et al [ 39 ] and Luo et al [ 40 ] and an accuracy comparison is demonstrated in Table 5 .…”

Section: Performance Evaluationmentioning

confidence: 99%

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Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model

Nazir

Nawaz

Rashid

et al. 2021

Sensors

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Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.

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Section: Methods Accuracy (%)mentioning

confidence: 81%

Section: Performance Evaluationmentioning

confidence: 99%

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model

Nazir

Nawaz

Rashid

et al. 2021

Sensors

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“…In this experiment, we compared the model proposed with the state-of-the-art DR severity classification model and proved the effectiveness of our adaptively weighted fusion network of DR severity classification. Table 7 shows the performance comparison between the model obtained and the models used in [23][24][25]. We can see that the performance evaluation of the proposed method in this paper is significantly better than the existing models in the literature.…”

Section: Performance Comparison Of the Proposed Methods With State Of The Artmentioning

confidence: 85%

“…We can see that the performance evaluation of the proposed method in this paper is significantly better than the existing models in the literature. Compared with the model using VGG with feature fusion in [25], we also achieved 0.97 on AUC but improved accuracy and kappa score. Figure 9 shows the confusion matrix when our model is applied to the DR severity prediction task.…”

Section: Performance Comparison Of the Proposed Methods With State Of The Artmentioning

confidence: 90%

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Multi-Scale Feature Fusion with Adaptive Weighting for Diabetic Retinopathy Severity Classification

2021

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Diabetic retinopathy (DR) is the prime cause of blindness in people who suffer from diabetes. Automation of DR diagnosis could help a lot of patients avoid the risk of blindness by identifying the disease and making judgments at an early stage. The main focus of the present work is to propose a feasible scheme of DR severity level detection under the MobileNetV3 backbone network based on a multi-scale feature of the retinal fundus image and improve the classification performance of the model. Firstly, a special residual attention module RCAM for multi-scale feature extraction from different convolution layers was designed. Then, the feature fusion by an innovative operation of adaptive weighting was carried out in each layer. The corresponding weight of the convolution block is updated in the model training automatically, with further global average pooling (GAP) and division process to avoid over-fitting of the model and removing non-critical features. In addition, Focal Loss is used as a loss function due to the data imbalance of DR images. The experimental results based on Kaggle APTOS 2019 contest dataset show that our proposed method for DR severity classification achieves an accuracy of 85.32%, a kappa statistic of 77.26%, and an AUC of 0.97. The comparison results also indicate that the model obtained is superior to the existing models and presents superior classification performance on the dataset.

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Analysis of Pre-trained Convolutional Neural Network Models in Diabetic Retinopathy Detection Through Retinal Fundus Images

Escorcia‐Gutierrez

Cuello-Navarro

Barraza

et al. 2022

Computer Information Systems and Industrial Management

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Diabetic Retinopathy (DR) is a disease on the rise; as this is a complication of diabetes, it becomes an imminent fate in people who have not been treated correctly for the disease, resulting in possible loss of vision if not is detected in time. This disease affects the retina, and the diagnosis is made based on fundus images of patients, through which various lesions and anomalies can be visualized. Visual inspection is a challenging task, and the diagnosis is expert dependent. This article proposes a convolutional neural network (CNN) model to detect DR, a common illness in diabetic patients. This work allows estimating the capacity of a pre-trained CNN (VGG16) using the transfer learning technique to detect symptoms and injuries caused by DR. For learning and feature extraction we used a set of retinal images obtained from the APTOS 2019 Blindness Detection competition in Kaggle. This network is trained and learns to identify between healthy retina and RD with high performance, overcoming other works. The best experimentation we obtained reached an accuracy value of 96.86% for DR detection tasks.

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Deep convolution feature aggregation: an application to diabetic retinopathy severity level prediction

Cited by 46 publications

References 28 publications

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model

Multi-Scale Feature Fusion with Adaptive Weighting for Diabetic Retinopathy Severity Classification

Analysis of Pre-trained Convolutional Neural Network Models in Diabetic Retinopathy Detection Through Retinal Fundus Images

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