Deep learning algorithms deployed on mobile devices to screen for eye disease

The so-called 'lightweight' deep learning (DL) model can be trained with a small number of images, including ones with a high degree of noise, and can be used on mobile devices.

Details of the research have been published in Scientific Reports.

According to Tohoku University, DL model reliant self-monitoring and tele-screening of diseases are becoming more routine but deep learning algorithms are generally task specific, identifying or detecting general objects such as humans, animals, or road signs.

Identifying diseases demands precise measurement of tumours, tissue volume, or other sorts of abnormalities. To do so requires a model to look at separate images and mark boundaries in a process called segmentation. Accurate prediction takes greater computational output, rendering them difficult to deploy on mobile devices.

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"There is always a trade-off between accuracy, speed and computational resources when it comes to DL models," said Toru Nakazawa, co-author of the study and professor at Tohoku University's Department of Ophthalmology. "Our developed model has better segmentation accuracy and enhanced model training reproducibility, even with fewer parameters - making it efficient and more lightweight when compared to other commercial softwares."