Domain-specific trained model to auto grade AV Nicking severity level

Lakshmi Kala Pampana, Manjula Sri Rayudu

Abstract: ArterioVenous Nicking (AVN) is a prominent vascular biomarker in predicting cardiovascular risk factors such as atherosclerosis, hypertension, coronary artery diseases, and heart stroke. The morphological change in the retinal junction calibre presents various clues to diagnose the aforesaid risk factors. In this paper, we introduced a novel hierarchical training strategy to improve the grading accuracy in assessing the abnormality. In the first level, a domain-specific weights are generated from the pretrained weights. At the second level, domain-specific weights are transferred to the target architecture called Scale Attention Region-Based Deep Architecture, which is again trained on the AV Nicked dataset. In the target architecture, the scale attention feature extractor generates adaptive weights based on the semantic information at different levels of resolution. The advantage of this module is that it extracts minute features of severity so that the grading can be done precisely. The hierarchically trained classifier’s performance for AVN grading is compared with manual grading and also with a generalised pretrained(PT) model. The ResNet101 + DST metrics are evaluated and compared with a ground truth dataset.