The set of algorithms is made up of two components: a deep convolutional neural network and an attention-guided network. The former mimics the human brain’s biological process to adapt to learning new things, while the attention-guided network imitates the brain’s manner of selectively focusing on a few relevant features – in this case, the optic nerve head region in the fundus images. The outputs from these two components are then fused together to generate the final prediction result.
To test their algorithms, the scientists first reduced the resolution of 282 fundus images (70 glaucoma cases and 212 healthy cases) taken of TTSH patients during their eye screening, before training the algorithms with 70 per cent of the dataset.
To generate more training samples, the scientists also applied image augmentation – a technique that involves applying random but realistic transformations, such as image rotation – to increase the diversity of the dataset used to train the algorithms, which enhances the algorithms’ classification accuracy.
The study exemplifies NTU’s research efforts as part of its 2025 strategic plan to be at the forefront of tackling four of humanity’s grand challenges, one of which is to respond to the needs and challenges of healthy living and ageing.
Associate Professor Wang Lipo from the NTU School of Electrical and Electronic Engineering and lead author of the study said: "Through a combination of machine learning techniques, our team has developed a screening model that can diagnose glaucoma from fundus images, removing the need for ophthalmologists to take various clinical measurements (such as internal eye pressure) for diagnosis. The ease of use of our robust automated glaucoma diagnosis approach means that any healthcare practitioner could make use of the system to help in glaucoma screening. This will be especially helpful in geographical areas with less access to ophthalmologists."
The team is now testing their algorithms on a larger dataset of patient fundus images taken at TTSH. They are also looking at how the software can be ported to a mobile phone application so that, when used in conjunction with a fundus camera or lens adaptor for mobile phones, it could be a feasible glaucoma screening tool in the field.
The joint research team then tested their screening method on the remaining 30 per cent of the patient images and found that it had an accuracy of 97 per cent in correctly identifying glaucoma cases, and a sensitivity (the fraction of cases correctly classified among all positive glaucoma cases) of 95 per cent – higher than other state-of-the-art deep learning based-methods also trialled during the study, which yielded sensitivities ranging from 69 to 89 per cent.
The scientists also found that using a pair of stereo fundus images improved the sensitivity of their screening system. When single fundus images were used, the algorithms had a lower sensitivity of 85 to 86 per cent.
MEDICA-tradefair.com; Source: Nanyang Technological University
