A well-known retinal disease that sends blurry visions to the affected patients is Macular Degeneration. This research is based on classifying the healthy and macular degeneration fundus by 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 a 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 the affected area of the fundus.

翻译：黄斑变性是一种常见的视网膜疾病，会导致患者视力模糊。本研究基于眼底健康与黄斑变性图像的分类，并对病变区域进行定位。采用CNN架构以及以ResNet（ResNet50、ResNet50v2、ResNet101、ResNet101v2、ResNet152、ResNet152v2）为主干的CNN模型对两类眼底图像进行分类。数据按三种比例划分：（a）训练集90%、测试集10%；（b）训练集80%、测试集20%；（c）训练集50%、测试集50%。训练完成后，根据评估指标筛选出最佳模型。在所有模型中，以ResNet50为主干的CNN表现最优，在90%训练集与10%测试集的数据划分下，训练准确率达到98.7%。基于该模型，我们采用Grad-CAM可视化方法获取眼底病变区域的位置。