An Effective Method for Detecting and Classifying Diabetic Retinopathy Lesions Based on Deep Learning

Erciyas, Abdüssamed; Barışçı, Necaattin

doi:10.1155/2021/9928899

Cited by 37 publications

(11 citation statements)

References 65 publications

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“…To classify the severity level, this system completely relies on the quality of the images that are presented easily. Those images are retrained using the transfer learning method to detect the lesions easily [ 92 ]. To develop a system with minimal configuration, overfitting and computational cost need to be reduced.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Uppamma

Bhattacharya

2023

Journal of Healthcare Engineering

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Diabetic retinopathy (DR) is a common eye retinal disease that is widely spread all over the world. It leads to the complete loss of vision based on the level of severity. It damages both retinal blood vessels and the eye’s microscopic interior layers. To avoid such issues, early detection of DR is essential in association with routine screening methods to discover mild causes in manual initiation. But these diagnostic procedures are extremely difficult and expensive. The unique contributions of the study include the following: first, providing detailed background of the DR disease and the traditional detection techniques. Second, the various imaging techniques and deep learning applications in DR are presented. Third, the different use cases and real-life scenarios are explored relevant to DR detection wherein deep learning techniques have been implemented. The study finally highlights the potential research opportunities for researchers to explore and deliver effective performance results in diabetic retinopathy detection.

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Section: Discussionmentioning

confidence: 99%

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Uppamma

Bhattacharya

2023

Journal of Healthcare Engineering

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“…By the way, they also provide a simple method of addressing the imbalance of DR databases. Erciyas et al [24] develop a deep learning-based method in which diabetic retinopathy lesions are detected automatically and independently of datasets, and the detected lesions are classified. Vives et al [25] present a bio-inspired approach on synaptic metaplasticity in convolutional neural networks to detect diabetic retinopathy.…”

Section: A Retinal Image Classificationmentioning

confidence: 99%

Graph Adversarial Transfer Learning for Diabetic Retinopathy Classification

Jing-bo

Wang

et al. 2022

IEEE Access

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Diabetic retinopathy (DR) is an essential factor that has caused vision loss and even blindness in middle-aged and older adults. A system that can automatically perform DR diagnosis can help ophthalmologists save a lot of tedious work, such as DR grading or lesion detection. At the same time, patients can find their diseases earlier and perform the correct treatment. However, most of the existing methods require many DR annotations to train the model, and the DR data will vary to different degrees due to various shooting tools. The above problems lead to the inefficient use of existing data in the experiment, limiting actual deployment. To alleviate this problem, we propose a novel Graph Adversarial Transfer Learning (GATL) for DR diagnosis in a deep model through transfer learning, including intradomain alignment and inter-domain alignment. The proposed GATL enjoys several merits. First, our GATL adopts the self-supervised training to save the annotating cost in the target domain thus this domain adaptation method can significantly reduce annotation cost compared to the supervised approaches. Second, we introduce the graph neural network to extract potential features between unknown samples. Third, to enhance the robustness of the model, we use adversarial training to perform both inter-domain and intradomain alignment to further improve the model's classification accuracy. GATL achieved 94.3%, 97.5%, and 91.1% in accuracy, sensitivity, and specificity in the APTOS dataset and 92.7%, 95.7%, and 89.7% in the EyePACS dataset, respectively. Extensive experimental results on two challenging benchmarks, including APTOS 2019 and EyePACS, demonstrate that the proposed GATL performs favorably against baseline DR classification methods.

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“…A process of normalization and augmentation, automated feature extraction, and pixelwise label prediction were performed, therefore improving both precision and recall. Deep learning was employed in [21] for DR detection. The images obtained were further classified with the aid of transfer learning and attention mechanisms.…”

Section: Related Workmentioning

confidence: 99%

Luminosity Normalized Symmetric Deep Convolute Tubular Classifier for Diabetic Retinopathy Detection

Muthusamy

Palani

2022

Preprint

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Diabetic retinopathy (DR) is a common eye disease and a notable starting point of blindness in diabetic patients. Detecting the existence of microaneurysms in the fundus images and the identification of DR in the preliminary stage has always been a considerable question for decades. Systematic screening and appropriate interference are the most efficient mechanism for disease management. The sizeable populations of diabetic patients and their enormous screening requirements have given rise to the computer-aided and automatic diagnosis of DR. The utilizationof Deep Neural Networks in DR diagnosis has also attracted much attention and considerable advancement has been made. However, despite the several advancements that have been made, there remains room for improvement in the sensitivity and specificity of the DR diagnosis. In this work, a novel method called the Luminosity Normalized Symmetric Deep Convolute Tubular Classifier (LN-SDCTC) for DR detection is proposed. The LN-SDCTC method is split into two parts. Initially, with the retinal colorfundus images obtained as input, the Luminosity Normalized Retinal Color Fundus Preprocessing model is applied to produce a noise-minimized enhanced contrast image. Second, the obtained processed image is provided as input to the Symmetric Deep Convolute network. Here, with the aid of the convolutional layer (i.e., the Tubular Neighborhood Window), the average pooling layer (i.e., average magnitude value of tubular neighbors), and the max-pooling layer (i.e., maximum contrast orientation), relevant features are selected. Finally, with the extracted features as input and with the aid of the Multinomial Regression Classification function, the severity of the DR disease is determined. Extensive experimental results in terms of peak signal-to-noise ratio, disease detection time, sensitivity, and specificity reveal that the proposed method of DR detection greatly facilitates the deep learning model and yields better results than various state-of-art methods.

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An Effective Method for Detecting and Classifying Diabetic Retinopathy Lesions Based on Deep Learning

Cited by 37 publications

References 65 publications

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Graph Adversarial Transfer Learning for Diabetic Retinopathy Classification

Luminosity Normalized Symmetric Deep Convolute Tubular Classifier for Diabetic Retinopathy Detection

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