ArticleBiomedical engineering online2017
Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.
Article in Biomedical engineering online, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Structural brain correlates of balance control in children with cerebral palsy: baseline correlations and effects of training.Brain structure & function · 2025Trial
- Machine Learning-Based Classification of Retinitis Pigmentosa from Color Fundus Images: A Reproducible Benchmark and Screening-Oriented Pipeline.Vision (Basel, Switzerland) · 2026Article
- HM_ADET: a hybrid model for automatic detection of eyelid tumors based on photographic images.Biomedical engineering online · 2024Article
- Development and Validation of Multivariable Prediction Algorithms to Estimate Future Walking Behavior in Adults: Retrospective Cohort Study.JMIR mHealth and uHealth · 2023Article
- Using machine learning to determine the correlation between physiological and environmental parameters and the induction of acute mountain sickness.BMC bioinformatics · 2022Article
- Automatic Left Ventricle Segmentation from Short-Axis Cardiac MRI Images Based on Fully Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- General deep learning model for detecting diabetic retinopathy.BMC bioinformatics · 2021Article
- Classification of imbalanced oral cancer image data from high-risk population.Journal of biomedical optics · 2021Article
- Automatic anatomical classification of colonoscopic images using deep convolutional neural networks.Gastroenterology report · 2021Article
- Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study.JMIR medical informatics · 2021Article
- Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study.JMIR medical informatics · 2021Article
- Pathological Myopia Image Recognition Strategy Based on Data Augmentation and Model Fusion.Journal of healthcare engineering · 2021Article
- Improving the Generalizability of Infantile Cataracts Detection via Deep Learning-Based Lens Partition Strategy and Multicenter Datasets.Frontiers in medicine · 2021Article
- Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization.Annals of translational medicine · 2020Review
- Imbalanced biomedical data classification using self-adaptive multilayer ELM combined with dynamic GAN.Biomedical engineering online · 2018Article
- Predicting the progression of ophthalmic disease based on slit-lamp images using a deep temporal sequence network.PloS one · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
Abstract
backgroundOcular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial.
methodsIn this paper, we develop an effective cost-sensitive deep residual convolutional neural network (CS-ResCNN) classifier to diagnose ophthalmic diseases using retro-illumination images. First, the regions of interest (crystalline lens) are automatically identified via twice-applied Canny detection and Hough transformation. Then, the localized zones are fed into the CS-ResCNN to extract high-level features for subsequent use in automatic diagnosis. Second, the impacts of cost factors on the CS-ResCNN are further analyzed using a grid-search procedure to verify that our proposed system is robust and efficient.
resultsQualitative analyses and quantitative experimental results demonstrate that our proposed method outperforms other conventional approaches and offers exceptional mean accuracy (92.24%), specificity (93.19%), sensitivity (89.66%) and AUC (97.11%) results. Moreover, the sensitivity of the CS-ResCNN is enhanced by over 13.6% compared to the native CNN method.
conclusionOur study provides a practical strategy for addressing imbalanced ophthalmological datasets and has the potential to be applied to other medical images. The developed and deployed CS-ResCNN could serve as computer-aided diagnosis software for ophthalmologists in clinical application.
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.