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Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.
- Source :
-
BioMedical Engineering OnLine . 11/21/2017, Vol. 16, p1-20. 20p. - Publication Year :
- 2017
-
Abstract
- <bold>Background: </bold>Ocular 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.<bold>Methods: </bold>In 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.<bold>Results: </bold>Qualitative 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.<bold>Conclusion: </bold>Our 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1475925X
- Volume :
- 16
- Database :
- Academic Search Index
- Journal :
- BioMedical Engineering OnLine
- Publication Type :
- Academic Journal
- Accession number :
- 126358572
- Full Text :
- https://doi.org/10.1186/s12938-017-0420-1