using deep learning to automate ocular toxoplasmosis diagnosis from retina images
Inspired by the success of CNNs in identifying retinal entities associated with ocular diseases such as diabetic retinopathy, this study implemented a CNN architecture for ocular toxoplasmosis image classification. For this project, three sources of images were merged to create a dataset of 2464 retinal images, of which 2079 are healthy and 385 are unhealthy.
grant 2022/11378-3, São Paulo Research Foundation (FAPESP)
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