FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Grading

• URL: http://arxiv.org/abs/2205.01676v3
• Date: Tue, 6 Jun 2023 19:41:33 GMT
• Title: FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Grading
• Authors: Or Abramovich, Hadas Pizem, Jan Van Eijgen, Ilan Oren, Joshua Melamed, Ingeborg Stalmans, Eytan Z. Blumenthal and Joachim A. Behar
• Abstract summary: Glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and vision impairment. Key step in this process is to automatically estimate the quality of the fundus images to make sure these are interpretable by a human operator or a machine learning model. We present a novel fundus image quality scale and deep learning (DL) model that can estimate fundus image quality relative to this new scale.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Objective: Ophthalmological pathologies such as glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and vision impairment. There is a need for novel decision support tools that can simplify and speed up the diagnosis of these pathologies. A key step in this process is to automatically estimate the quality of the fundus images to make sure these are interpretable by a human operator or a machine learning model. We present a novel fundus image quality scale and deep learning (DL) model that can estimate fundus image quality relative to this new scale. Methods: A total of 1,245 images were graded for quality by two ophthalmologists within the range 1-10, with a resolution of 0.5. A DL regression model was trained for fundus image quality assessment. The architecture used was Inception-V3. The model was developed using a total of 89,947 images from 6 databases, of which 1,245 were labeled by the specialists and the remaining 88,702 images were used for pre-training and semi-supervised learning. The final DL model was evaluated on an internal test set (n=209) as well as an external test set (n=194). Results: The final DL model, denoted FundusQ-Net, achieved a mean absolute error of 0.61 (0.54-0.68) on the internal test set. When evaluated as a binary classification model on the public DRIMDB database as an external test set the model obtained an accuracy of 99%. Significance: the proposed algorithm provides a new robust tool for automated quality grading of fundus images.

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