DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM

• URL: http://arxiv.org/abs/2108.08095v1
• Date: Wed, 18 Aug 2021 11:36:37 GMT
• Title: DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM
• Authors: Farzan Shenavarmasouleh, Farid Ghareh Mohammadi, M. Hadi Amini, Thiab Taha, Khaled Rasheed, Hamid R. Arabnia
• Abstract summary: We propose a new lesion detection architecture, comprising of two sub-modules, which is an optimal solution to detect and find lesions caused by Diabetic Retinopathy (DR) We also use two popular evaluation criteria to evaluate the outputs of our models, which are intersection over union (IOU) and mean average precision (mAP) We hypothesize that this new solution enables specialists to detect lesions with high confidence and estimate the severity of the damage with high accuracy.
• Score: 2.9360071145551068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical Imaging is one of the growing fields in the world of computer vision. In this study, we aim to address the Diabetic Retinopathy (DR) problem as one of the open challenges in medical imaging. In this research, we propose a new lesion detection architecture, comprising of two sub-modules, which is an optimal solution to detect and find not only the type of lesions caused by DR, their corresponding bounding boxes, and their masks; but also the severity level of the overall case. Aside from traditional accuracy, we also use two popular evaluation criteria to evaluate the outputs of our models, which are intersection over union (IOU) and mean average precision (mAP). We hypothesize that this new solution enables specialists to detect lesions with high confidence and estimate the severity of the damage with high accuracy.

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