A data-driven approach to referable diabetic retinopathy detection.
Artif Intell Med
; 96: 93-106, 2019 05.
Article
em En
| MEDLINE
| ID: mdl-31164214
ABSTRACT
Prior art on automated screening of diabetic retinopathy and direct referral decision shows promising performance; yet most methods build upon complex hand-crafted features whose performance often fails to generalize. OBJECTIVE:
We investigate data-driven approaches that extract powerful abstract representations directly from retinal images to provide a reliable referable diabetic retinopathy detector.METHODS:
We gradually build the solution based on convolutional neural networks, adding data augmentation, multi-resolution training, robust feature-extraction augmentation, and a patient-basis analysis, testing the effectiveness of each improvement.RESULTS:
The proposed method achieved an area under the ROC curve of 98.2% (95% CI 97.4-98.9%) under a strict cross-dataset protocol designed to test the ability to generalize - training on the Kaggle competition dataset and testing using the Messidor-2 dataset. With a 5â¯×â¯2-fold cross-validation protocol, similar results are achieved for Messidor-2 and DR2 datasets, reducing the classification error by over 44% when compared to most published studies in existing literature.CONCLUSION:
Additional boost strategies can improve performance substantially, but it is important to evaluate whether the additional (computation- and implementation-) complexity of each improvement is worth its benefits. We also corroborate that novel families of data-driven methods are the state of the art for diabetic retinopathy screening.SIGNIFICANCE:
By learning powerful discriminative patterns directly from available training retinal images, it is possible to perform referral diagnostics without detecting individual lesions.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Redes Neurais de Computação
/
Retinopatia Diabética
Tipo de estudo:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Artif Intell Med
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2019
Tipo de documento:
Article