Deep Learning for The Automatic Diagnosis and Detection of Multiple Retinal Abnormalities

Article type: Research Article

Authors: Gian, Michelle K.S.^{; *} | Raman, Valliappan | Then, Patrick H.H.

Affiliations: Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak, Kuching, Malaysia

Correspondence: [*] Corresponding author. Email: [email protected]

Abstract: This paper presents a fully-automated method to detect retinal abnormalities in both a global and local context by generating: (1) a disease label with a probability score and (2) a 4-channel pixel-level segmentation map of retinal lesions. The characteristics of retinal abnormalities, which occur as various shapes, sizes, and distribution at different regions, are a challenge in accomplishing these tasks. In addition, the small amount of image-level labelled images in public databases and the unavailability of lesion-level annotations for most of these publicly available images also pose as challenges. These shortcomings motivate our exploration of various CNN architectures to extract multi-scale contextual information, such that we investigate the impact of different arrangements of multi-sized convolutional kernels appended to a modified pre-trained encoder. Additionally, to prevent the loss of detailed information for small lesions, we exploit the advantages of feature map concatenation from the output of these multi-scale convolutions to its corresponding decoder layer. A new two-phase training strategy is also implemented to tackle the problem of dataset imbalance between image-level label and lesion-level label classes. The direct comparison between our proposed methods and currently published state-of-the-art methods with the same databases confirms that our best model outperforms existing published methods.

Keywords: Convolutional neural network, retinal abnormalities, segmentation, classification, multi-scale convolutions

DOI: 10.3233/JID190017

Journal: Journal of Integrated Design and Process Science, vol. 23, no. 3, pp. 5-41, 2019