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

Uppamma, Posham; Bhattacharya, Sweta

doi:10.1155/2023/2728719

Cited by 20 publications

(9 citation statements)

References 88 publications

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“…Their accuracy in identifying this disease has been outstanding, often outperforming human specialists. This study uses AlexNet and Resnet101 feature extraction techniques to automatically recognize and categorize DR fundus pictures based on severity, allowing patients with high blood sugar-related DR to avoid complications and potentially lose their eyesight ( Fayyaz et al, 2023 ; Uppamma & Bhattacharya, 2023 ). Early diagnosis through automatic detection utilizing modern technology can help prevent consequences like eyesight loss.…”

Section: Deep Learning In Diabetic Retinopathymentioning

confidence: 99%

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

Bhulakshmi,

Rajput

2024

PeerJ Computer Science

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Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.

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Section: Deep Learning In Diabetic Retinopathymentioning

confidence: 99%

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

Bhulakshmi,

Rajput

2024

PeerJ Computer Science

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“…Several research papers have investigated the prediction of DR utilizing CNN and pre-trained models, including DenseNet, VGG, ResNet, and Inception [19]. Moreover, specific study is required to enhance the efficiency of the deep learning framework [20]. With 94.4% accuracy, Usman et al [4] observed that a pre-trained CNN model detected lesions better.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Model for Detecting the Diabetic Retinopathy Stages with Discrete Wavelet Transform

Mutawa,

Al-Sabti,

Raizada

et al. 2024

Preprint

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Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetic people. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today's growing medical field poses more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model's capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image undergoes preprocessing using contrast-limited adaptive histogram equalization (CLAHE) and the discrete wavelet transform (DWT). The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy (DDR) and the Asia Pacific Tele-Ophthalmology Society (APTOS). The CNN model is tuned with different learning rates and optimizers. The CNN model with Adam optimizer achieved 70% accuracy and an area under curve (AUC) score of 0.82. The recommended study results may reduce diabetes-related vision impairment by early DR severity identification.

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“…Several research papers have investigated the prediction of DR, utilizing CNN and pretrained models, including DenseNet, Visual Geometry Group (VGG), ResNet, and Inception [19]. Moreover, specific study is required to enhance the efficiency of the deep learning framework [20]. Usman et al [4] observed that a pretrained CNN model detected lesions better, with 94.4% accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform

Mutawa,

Al-Sabti,

Raizada

et al. 2024

Applied Sciences

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Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetics. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today’s growing medical field presents a more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model’s capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image is preprocessed using contrast-limited adaptive histogram equalization and the discrete wavelet transform. The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy and the Asia-Pacific Tele-Ophthalmology Society. The CNN model is tuned in with different learning rates and optimizers. An accuracy of 72% and an area under curve score of 0.90 was achieved by the CNN model with the Adam optimizer. The recommended study results may reduce diabetes-related vision impairment by early identification of DR severity.

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

Cited by 20 publications

References 88 publications

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

A Deep Learning Model for Detecting the Diabetic Retinopathy Stages with Discrete Wavelet Transform

A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform

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