Abstract
Medical imaging has come out to be very challenging field, in the area of computer vision; in this study, it has been aimed to detect diabetic retinopathy with image identification and segmentation techniques. Diabetic retinopathy (DR) is an eye disease caused by diabetes that can proceed to cause blindness; therefore, early detection is very critical to prevent visual disturbances. Evaluation will be done on the basis of mean average precision (MAP), aiming to detect lesions with the pretrained models like Mask-RCNN instance segmentation (R-50/R-101/X-101). It will finally come to the conclusion after custom training and testing on all set of real-life retina images taken from the clinical dataset and get a properly segmented region with nearly accurate bounding box. The model which identifies the accurate lesions caused by diabetic retinopathy with the disease named as “exudates” and “microaneurysms” will be considered for the future references.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Aryan, Deb, S. (2023). Identification of Diabetic Retinopathy Using Robust Segmentation Through Mask RCNN. In: Das, A.K., Nayak, J., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. CIPR 2022. Lecture Notes in Networks and Systems, vol 725. Springer, Singapore. https://doi.org/10.1007/978-981-99-3734-9_4
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DOI: https://doi.org/10.1007/978-981-99-3734-9_4
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