Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.

Abstract:

BACKGROUND: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. METHODS: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. RESULTS: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. CONCLUSION: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.

journal_name

Biomed Eng Online

authors

Jiang J,Liu X,Zhang K,Long E,Wang L,Li W,Liu L,Wang S,Zhu M,Cui J,Liu Z,Lin Z,Li X,Chen J,Cao Q,Li J,Wu X,Wang D,Wang J,Lin H

doi

10.1186/s12938-017-0420-1

subject

Has Abstract

pub_date

2017-11-21 00:00:00

pages

132

issue

1

issn

1475-925X

pii

10.1186/s12938-017-0420-1

journal_volume

16

pub_type

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