论文标题
DRDR II：使用面膜RCNN和转移学习检测糖尿病性视网膜病的严重程度
DRDr II: Detecting the Severity Level of Diabetic Retinopathy Using Mask RCNN and Transfer Learning
论文作者
论文摘要
DRDR II是机器学习和深度学习世界的混合体。它建立在其先前的成功基础上，即经过训练，可以检测，定位和创建分割面膜，以供两种类型的病变（渗出剂和微型神经瘤），可以在糖尿病性视网膜病（DR）患者眼中找到。并将整个模型用作管道核心中的固体特征提取器来检测DR病例的严重程度。我们采用了一个大数据集，其中包含超过35,000张从全球收集的底底图像，以及在特征提取的2阶段进行预处理后，我们成功地预测了正确的严重性水平，精度超过92％。
DRDr II is a hybrid of machine learning and deep learning worlds. It builds on the successes of its antecedent, namely, DRDr, that was trained to detect, locate, and create segmentation masks for two types of lesions (exudates and microaneurysms) that can be found in the eyes of the Diabetic Retinopathy (DR) patients; and uses the entire model as a solid feature extractor in the core of its pipeline to detect the severity level of the DR cases. We employ a big dataset with over 35 thousand fundus images collected from around the globe and after 2 phases of preprocessing alongside feature extraction, we succeed in predicting the correct severity levels with over 92% accuracy.
