Deep learning-based hemorrhage detection for diabetic retinopathy screening

Aziz, Tamoor; Charoenlarpnopparut, Chalie; Mahapakulchai, Srijidtra

doi:10.1038/s41598-023-28680-3

Cited by 23 publications

(8 citation statements)

References 32 publications

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“…Although this is the first study that investigates distinguishing etiologies of retinal hemorrhages from fundus photos, there have been previous studies focused on detecting the presence, segmentation, or identifying the layer of retinal hemorrhages from fundus images [40][41][42][43].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study

Khosravi,

Huck,

Shahraki

et al. 2023

IJMS

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Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AI’s potential in diagnosing etiologies of pediatric retinal hemorrhages.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study

Khosravi,

Huck,

Shahraki

et al. 2023

IJMS

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“…Aziz et al 28 proposed a novel methodology for hemorrhage detection. First, they enhanced the quality of the image, using contrast limited adaptive histogram equalization to improve the contrast of an image.…”

Section: Related Workmentioning

confidence: 99%

SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation

Ullah

Usman

Latif

et al. 2023

Sci Rep

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Diabetic retinopathy (DR) is a diabetes complication that can cause vision loss among patients due to damage to blood vessels in the retina. Early retinal screening can avoid the severe consequences of DR and enable timely treatment. Nowadays, researchers are trying to develop automated deep learning-based DR segmentation tools using retinal fundus images to help Ophthalmologists with DR screening and early diagnosis. However, recent studies are unable to design accurate models due to the unavailability of larger training data with consistent and fine-grained annotations. To address this problem, we propose a semi-supervised multitask learning approach that exploits widely available unlabelled data (i.e., Kaggle-EyePACS) to improve DR segmentation performance. The proposed model consists of novel multi-decoder architecture and involves both unsupervised and supervised learning phases. The model is trained for the unsupervised auxiliary task to effectively learn from additional unlabelled data and improve the performance of the primary task of DR segmentation. The proposed technique is rigorously evaluated on two publicly available datasets (i.e., FGADR and IDRiD) and results show that the proposed technique not only outperforms existing state-of-the-art techniques but also exhibits improved generalisation and robustness for cross-data evaluation.

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“…The algorithm used is the VGG16 model; the current work suggests modifying the VGG [52,53] model to get better outcomes and achieve better results. In VGG16, only the ImageNet dataset was used for pre-training the model.…”

Section: The Proposed Algorithm Of Tl and Cnn Architecturementioning

confidence: 99%

Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

El-Hoseny,

Elsepae,

Mohamed

et al. 2023

CMC

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Diabetic retinopathy is a critical eye condition that, if not treated, can lead to vision loss. Traditional methods of diagnosing and treating the disease are time-consuming and expensive. However, machine learning and deep transfer learning (DTL) techniques have shown promise in medical applications, including detecting, classifying, and segmenting diabetic retinopathy. These advanced techniques offer higher accuracy and performance. Computer-Aided Diagnosis (CAD) is crucial in speeding up classification and providing accurate disease diagnoses. Overall, these technological advancements hold great potential for improving the management of diabetic retinopathy. The study's objective was to differentiate between different classes of diabetes and verify the model's capability to distinguish between these classes. The robustness of the model was evaluated using other metrics such as accuracy (ACC), precision (PRE), recall (REC), and area under the curve (AUC). In this particular study, the researchers utilized data cleansing techniques, transfer learning (TL), and convolutional neural network (CNN) methods to effectively identify and categorize the various diseases associated with diabetic retinopathy (DR). They employed the VGG-16CNN model, incorporating intelligent parameters that enhanced its robustness. The outcomes surpassed the results obtained by the auto enhancement (AE) filter, which had an ACC of over 98%. The manuscript provides visual aids such as graphs, tables, and techniques and frameworks to enhance understanding. This study highlights the significance of optimized deep TL in improving the metrics of the classification of the four separate classes of DR. The manuscript emphasizes the importance of using the VGG16CNN classification technique in this context.

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Deep learning-based hemorrhage detection for diabetic retinopathy screening

Cited by 23 publications

References 32 publications

Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study

Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study

SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation

Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

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