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. 2020 Aug 1;10(4):507-512.
doi: 10.31661/jbpe.v0i0.2003-1080. eCollection 2020 Aug.

A Deep Learning Approach to Automatic Recognition of Arcus Senilis

Amini N  1 , Ameri A  2
Affiliations

A Deep Learning Approach to Automatic Recognition of Arcus Senilis

Amini N et al. J Biomed Phys Eng. .

Abstract

Background: Arcus Senilis (AS) appears as a white, grey or blue ring or arc in front of the periphery of the iris, and is a symptom of abnormally high cholesterol in patients under 50 years old.

Objective: This work proposes a deep learning approach to automatic recognition of AS in eye images.

Material and methods: In this analytical study, a dataset of 191 eye images (130 normal, 61 with AS) was employed where ¾ of the data were used for training the proposed model and ¼ of the data were used for test, using a 4-fold cross-validation. Due to the limited amount of training data, transfer learning was conducted with AlexNet as the pretrained network.

Results: The proposed model achieved an accuracy of 100% in classifying the eye images into normal and AS categories.

Conclusion: The excellent performance of the proposed model despite limited training set, demonstrate the efficacy of deep transfer learning in AS recognition in eye images. The proposed approach is preferred to previous methods for AS recognition, as it eliminates cumbersome segmentation and feature engineering processes.

Keywords: Arcus Senilis; Classification; Deep Learning; Transfer Learning.

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Conflict of interest statement

Conflict of Interest: None

Figures

Figure 1
Figure 1
Samples of eye images (a) normal, (b) Arcus Senilis (AS)
Figure 2
Figure 2
The block diagram of the proposed method: AlexNet (with replaced three last layers) is first trained with data from 3 folds and then the trained model is tested on data from the remaining fold.
Figure 3
Figure 3
The proposed model loss function for the test set, during the training progress.
Figure 4
Figure 4
The confusion matrix of the proposed model for one fold.
See this image and copyright information in PMC

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