Abstract
Pathological myopia (PM), which results from degenerative changes in the sclera, choroid, and retinal pigment epithelium (RPE), is associated with irreversible vision loss. This study proposes automatically detecting PM or normal vision from input retina fundus image. We have experimented with various transfer learning models and the pre-processing steps using reinforcement learning (RL). The best results were achieved with our custom ResNet50 as a baseline model. It has achieved an AUC score of 0.9984 on the validation dataset provided by the PALM challenge, a Satellite Event of The IEEE International Symposium on Biomedical Imaging in Venice, Italy. This AUC score is among the top 3 performers in this challenge. As in medical domain, more accurate results are always in demand, and this score ensures that the model can be set up for a clinical application in future as a second opinion to ophthalmologists.
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Ali, S., Raut, S. (2023). Detection of Pathological Myopia from Fundus Images. In: Sharma, H., Shrivastava, V., Bharti, K.K., Wang, L. (eds) Communication and Intelligent Systems. ICCIS 2022. Lecture Notes in Networks and Systems, vol 686. Springer, Singapore. https://doi.org/10.1007/978-981-99-2100-3_17
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DOI: https://doi.org/10.1007/978-981-99-2100-3_17
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