This paper experiments with the number of fully-connected layers in a deep convolutional neural network as applied to the classification of fundus retinal images. The images analysed corresponded to the ODIR 2019 (Peking University International Competition on Ocular Disease Intelligent Recognition) [9], which included images of various eye diseases (cataract, glaucoma, myopia, diabetic retinopathy, age-related macular degeneration (AMD), hypertension) as well as normal cases. This work focused on the classification of Normal, Cataract, AMD and Myopia. The feature extraction (convolutional) part of the neural network is kept the same while the feature mapping (linear) part of the network is changed. Different data sets are also explored on these neural nets. Each data set differs from another by the number of classes it has. This paper hence aims to find the relationship between number of classes and number of fully-connected layers. It was found out that the effect of increasing the number of fully-connected layers of a neural networks depends on the type of data set being used. For simple, linearly separable data sets, addition of fully-connected layer is something that should be explored and that could result in better training accuracy, but a direct correlation was not found. However as complexity of the data set goes up(more overlapping classes), increasing the number of fully-connected layers causes the neural network to stop learning. This phenomenon happens quicker the more complex the data set is.
翻译:这个纸质实验是一个深层神经神经网络中完全连接的层数,用于对基金视像图像进行分类。分析的图像与ODIR 2019 (北京大学国际肌肉疾病智能识别竞赛) [9] 相匹配,其中包括各种眼病(白内障、青光眼、近视、糖尿病视网病、与年龄有关的肌肉畸形(AMD)、高血压)以及正常案例的图像。这项工作的重点是对正常、白内障、AMD和 Myopia进行分类。神经网络的特征提取(动态)部分保持不变,而网络的特征映射(线性)部分则被修改。这些神经网的特征映射(线性)部分也进行了不同的数据集探索。每个数据集与它拥有的班数不同。因此,本文件旨在找出班数和完全连接层数之间的关系。发现增加完全连接的神经网络层数的影响取决于数据结构的更精确性(动态) 正在被使用的是更精确的内层, 更精确性数据结构的精确性结果应该是更精确的内层, 更精确的相互连接性 。但是, 更精确性的数据序列应该被完全地被找到。