The most important part of digital image analysis is object classification. Nowadays, deep learning makes an enormous achievement in computer vision problems. So there has been a lot of interests in applying features learned by convolutional neural networks (CNNs) on general image recognition to more tasks such as object detection, segmentation and face recognition. Leukocoria detection is one of the serious challenges in infant retinal treatment. Leukocoria is represented as an abnormal white reflection appearing in the eyes of an infant suffering from retinoblastoma. This research proposes a deep Visual Geometry Group-net CNN classifier for automatic detection of leukocoria. The proposed classifier comprises pre-processing, feature extraction and classification. The deep CNN classifier contains convolution layer, pooling layer and fully connected layer with weights are developed on each image. Experimental results based on several eye images consist of ordinary and leukocoric from flicker, and it demonstrates that the proposed classifier provides better results with the accuracy of 98.5% and the error rate is below 2% which exceeds the current results.

中文翻译：

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基于深层VGG-net CNN技术的准确白细胞预测指标

数字图像分析的最重要部分是对象分类。如今，深度学习在计算机视觉问题上取得了巨大成就。因此，在将卷积神经网络（CNN）所学的特征应用于一般图像识别上，将其应用于诸如对象检测，分割和人脸识别等更多任务时，引起了很多兴趣。白细胞减少症的检测是婴儿视网膜治疗中的严重挑战之一。白细胞减少症表现为在患有视网膜母细胞瘤的婴儿的眼睛中出现的异常白色反射。这项研究提出了一种深度视觉几何组网CNN分类器，用于自动检测白斑。提出的分类器包括预处理，特征提取和分类。深度CNN分类器包含卷积层，在每个图像上展开具有权重的池化层和完全连接层。基于闪烁的普通眼和白皮质眼的几种眼睛图像的实验结果表明，提出的分类器提供了更好的结果，准确率达98.5％，错误率低于2％，超过了目前的结果。