Classification of Retinal Diseases Using Transfer Learning Approach

Das, Amrit; Giri, Rohan; Chourasia, Gunjan; Bala, Abubakar

doi:10.1109/icces45898.2019.9002415

Cited by 12 publications

(1 citation statement)

References 14 publications

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“…These diseases can be identified through classification, or can be segmented for deep analysis [6]. Glaucoma and related diseases can be classified using deep learning methods that can discriminate between normal and diseased eyes from large amounts of data [7]. Considering the importance of segmentation of retinal diseases, many researchers have focused on retinal vessels, optical disc, optical cup, optic nerve, and disease spot segmentation for analysis of different diseases [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa

Arsalan

Baek

Owais

et al. 2020

Sensors

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Ophthalmological analysis plays a vital role in the diagnosis of various eye diseases, such as glaucoma, retinitis pigmentosa (RP), and diabetic and hypertensive retinopathy. RP is a genetic retinal disorder that leads to progressive vision degeneration and initially causes night blindness. Currently, the most commonly applied method for diagnosing retinal diseases is optical coherence tomography (OCT)-based disease analysis. In contrast, fundus imaging-based disease diagnosis is considered a low-cost diagnostic solution for retinal diseases. This study focuses on the detection of RP from the fundus image, which is a crucial task because of the low quality of fundus images and non-cooperative image acquisition conditions. Automatic detection of pigment signs in fundus images can help ophthalmologists and medical practitioners in diagnosing and analyzing RP disorders. To accurately segment pigment signs for diagnostic purposes, we present an automatic RP segmentation network (RPS-Net), which is a specifically designed deep learning-based semantic segmentation network to accurately detect and segment the pigment signs with fewer trainable parameters. Compared with the conventional deep learning methods, the proposed method applies a feature enhancement policy through multiple dense connections between the convolutional layers, which enables the network to discriminate between normal and diseased eyes, and accurately segment the diseased area from the background. Because pigment spots can be very small and consist of very few pixels, the RPS-Net provides fine segmentation, even in the case of degraded images, by importing high-frequency information from the preceding layers through concatenation inside and outside the encoder-decoder. To evaluate the proposed RPS-Net, experiments were performed based on 4-fold cross-validation using the publicly available Retinal Images for Pigment Signs (RIPS) dataset for detection and segmentation of retinal pigments. Experimental results show that RPS-Net achieved superior segmentation performance for RP diagnosis, compared with the state-of-the-art methods.

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