ArticleSensors (Basel, Switzerland)2021
Performance Analysis of Deep-Neural-Network-Based Automatic Diagnosis of Diabetic Retinopathy.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 56 citations in OpenAlex.
- Current status and solutions for AI ethics in ophthalmology: a bibliometric analysis.NPJ digital medicine · 2025Article
- HIRD-Net: An Explainable CNN-Based Framework with Attention Mechanism for Diabetic Retinopathy Diagnosis Using CLAHE-D-DoG Enhanced Fundus Images.Life (Basel, Switzerland) · 2025Article
- A Performance Evaluation of Convolutional Neural Network Architectures for Pterygium Detection in Anterior Segment Eye Images.Diagnostics (Basel, Switzerland) · 2024Article
- Color fundus photograph-based diabetic retinopathy grading via label relaxed collaborative learning on deep features and radiomics features.Frontiers in cell and developmental biology · 2024Article
- Automated Diagnosis of Diabetic Retinopathy Using Deep Learning: On the Search of Segmented Retinal Blood Vessel Images for Better Performance.Bioengineering (Basel, Switzerland) · 2023Article
- A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet.Diagnostics (Basel, Switzerland) · 2023Article
- Research progress in artificial intelligence assisted diabetic retinopathy diagnosis.International journal of ophthalmology · 2023Article
- Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic.Frontiers in nutrition · 2023Article
- Deep neural network for prediction of diet quality among doctors and nurses in North China during the COVID-19 pandemic.Frontiers in public health · 2023Article
- Optical coherence tomography image based eye disease detection using deep convolutional neural network.Health information science and systems · 2022Article
- An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study.Sensors (Basel, Switzerland) · 2022Article
- Ultrasound image intelligent diagnosis in community-acquired pneumonia of children using convolutional neural network-based transfer learning.Frontiers in pediatrics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy (DR) is a human eye disease that affects people who are suffering from diabetes. It causes damage to their eyes, including vision loss. It is treatable; however, it takes a long time to diagnose and may require many eye exams. Early detection of DR may prevent or delay the vision loss. Therefore, a robust, automatic and computer-based diagnosis of DR is essential. Currently, deep neural networks are being utilized in numerous medical areas to diagnose various diseases. Consequently, deep transfer learning is utilized in this article. We employ five convolutional-neural-network-based designs (AlexNet, GoogleNet, Inception V4, Inception ResNet V2 and ResNeXt-50). A collection of DR pictures is created. Subsequently, the created collections are labeled with an appropriate treatment approach. This automates the diagnosis and assists patients through subsequent therapies. Furthermore, in order to identify the severity of DR retina pictures, we use our own dataset to train deep convolutional neural networks (CNNs). Experimental results reveal that the pre-trained model Se-ResNeXt-50 obtains the best classification accuracy of 97.53% for our dataset out of all pre-trained models. Moreover, we perform five different experiments on each CNN architecture. As a result, a minimum accuracy of 84.01% is achieved for a five-degree classification.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.