Ms. Catherine An Ha Profile

Catherine An Ha

at Babel Analytics

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Proceedings Article | 4 April 2022 Presentation + Paper

Applying a novel two-stage deep-learning model to improve accuracy in detecting retinal fundus images

Sai Kiran Reddy Maryada, William Lee Booker, Gopichandh Danala, Catherine An Ha, Sanjana Mudduluru, Dean Hougen, Bin Zheng

Proceedings Volume 12033, 120330K (2022) https://doi.org/10.1117/12.2611565

KEYWORDS: Performance modeling, Data modeling, Solid modeling, Photography, Computer aided diagnosis and therapy, Artificial intelligence, Systems modeling, Medical imaging, Imaging informatics, Visualization

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Applications of artificial intelligence (AI) in medical imaging informatics have attracted broad research interest. In ophthalmology, for example, automated analysis of retinal fundus photography helps diagnose and monitor illnesses like glaucoma, diabetic retinopathy, hypertensive retinopathy, and cancer. However, building a robust AI model requires a large and diverse dataset for training and validation. While large number of fundus photos are available online, collecting them to create a clean, well-structured dataset is a difficult and manually intensive process. In this work, we propose a two-stage deep-learning system to automatically identify clean retinal fundus images and delete images with severe artifacts. In two stages, two transfer-learning models based the ResNet-50 architecture pre-trained using ImageNet data are built with Increased threshold values on SoftMax to reduce false positives. The first stage classifier identifies “easy” images, and the remaining “difficult” (or undetermined) images are further identified by the second stage classifier. Using the Google Search Engine, we initially retrieve 1,227 retinal fundus images. Using this two-stage deep-learning model yields a positive predictive value (PPV) of 98.56% for the target class compared to a single-stage model with a PPV of 95.74%. The two-stage model helps reduce by two-thirds the false positives for the retinal fundus image class. The PPV over all classes increases from 91.9% to 96.6% without compromising the number of images classified by the model. The superior performance of this two-stage model indicates that the building of an optimal training dataset can play an important role in increasing performance of deep-learning models.

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