Abstract
A well-known retinal disease that feels blurry visions to the affected patients is Macular Degeneration. This research is based on classifying the healthy and macular degeneration fundus with localizing the affected region of the fundus. A CNN architecture and CNN with ResNet architecture (ResNet50, ResNet50v2, ResNet101, ResNet101v2, ResNet152, ResNet152v2) as the backbone are used to classify the two types of fundus. The data are split into three categories including (a) Training set is 90% and Testing set is 10% (b) Training set is 80% and Testing set is 20%, (c) Training set is 50% and Testing set is 50%. After the training, the best model has been selected from the evaluation metrics. Among the models, CNN with backbone of ResNet50 performs best which gives the training accuracy of 98.7\% for 90\% train and 10\% test data split. With this model, we have performed the Grad-CAM visualization to get the region of affected area of fundus.
Abstract (translated)
一种让受影响患者感觉模糊视觉的知名眼病是黄斑变性。这项研究基于将健康和黄斑变性眼底分为定位受影响的区域进行分类。使用卷积神经网络（CNN）架构和带ResNet架构的CNN（ResNet50，ResNet50v2，ResNet101，ResNet101v2，ResNet152，ResNet152v2）作为骨干网络对两种眼底进行分类。数据分为三个类别，包括（a）训练集占90%，测试集占10%；（b）训练集占80%，测试集占20%；（c）训练集占50%，测试集占50%。在训练后，从评估指标中选择最佳模型。在这些模型中，以ResNet50作为骨架的CNN表现最佳，其训练准确率为98.7% for 90% train and 10% test data split。使用这个模型，我们进行了Grad-CAM视觉化，以获取眼底的受影响区域。
URL
https://arxiv.org/abs/2404.15918
