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Pterygium-Net: a deep learning approach to pterygium detection and localization

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Abstract

Automatic pterygium detection is an essential screening tool for health community service groups. It allows non-expert to perform screening process without the needs of big and expensive equipment, especially for the application in rural areas. Thence, patients who have been screened as positive pterygium will be referred to the certified medical personnel for further diagnosis and treatment. Current state-of-the-art algorithms for pterygium detection rely on basic machine learning approach such as artificial neural network and support vector machine, which have not yet achieved high detection sensitivity and specificity as required in standard medical practice. Hence, a deep learning approach based on fully convolutional neural networks is proposed to detect and localize the pterygium infected tissues automatically. The input image requirement for the developed system is low as any commercial mobile phone camera is sufficient. Moreover, the developed algorithm, which we refer as Pterygium-Net works well even if the eye image is captured under low lighting condition with pupil position is not at the center location. Pterygium-Net utilizes three layers of convolutional neural networks (CNN) and three layers of fully connected networks. Two steps are implemented to overcome lacks of training data by generating synthetic images and pre-training the CNN weights and biases in a different public dataset. As for pterygium localization, an additional step of box proposal based on edges information is used to generate possible regions of the pterygium infected tissues. Hanning window is also applied to the generated regions to give more weightage to the center area. Experimental results show that Pterygium-Net produces high average detection sensitivity and specificity of 0.95 and 0.983, respectively. As for pterygium tissues localization, the algorithm achieves 0.811 accuracy with a very low failure rate of 0.053. In the future, deeper networks can be implemented to further improve pterygium localization.

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Authors and Affiliations

  1. Centre for Integrated Systems Engineering and Advanced Technologies, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia

    Mohd Asyraf Zulkifley & Siti Raihanah Abdani

  2. Community Health Department, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia

    Nuraisyah Hani Zulkifley

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  1. Mohd Asyraf Zulkifley
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  2. Siti Raihanah Abdani
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  3. Nuraisyah Hani Zulkifley
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Correspondence to Mohd Asyraf Zulkifley.

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The authors would like to acknowledge fundings from Universiti Kebangsaan Malaysia (Dana Impak Perdana: DIP-2015-006 and Geran Universiti Penyelidikan: GUP-2015-053). The Titan V used for this research was donated by the NVIDIA Corporation (KK-2019-005).

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Zulkifley, M.A., Abdani, S.R. & Zulkifley, N.H. Pterygium-Net: a deep learning approach to pterygium detection and localization. Multimed Tools Appl 78, 34563–34584 (2019). https://doi.org/10.1007/s11042-019-08130-x

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