Abstract
Corneal diseases are the most common eye disorders. Deep learning techniques are used to per-form automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients.
Abstract (translated)
角膜病是最常见的眼疾病。深度学习技术被用于自动诊断角膜。深度学习网络需要大规模的标注数据,这是深度学习的一个弱点。在这项工作中,介绍了使用条件生成对抗网络(CGANs)进行医学图像合成的方法。该方法还展示了如何利用生成的医学图像来丰富医学数据、改善临床决策,并提高传统神经网络(CNN)用于医学图像诊断的性能。研究包括使用角膜地形图从角膜病患者中通过Pentacam设备采集数据。该数据集包含3448个不同的角膜图像。此外,它展示了不平衡数据集如何影响分类器的性能,在该数据集上使用插值方法进行数据平衡。最后,训练在平衡数据集上的CNN网络的结果与训练在不平衡数据集上的CNN网络的结果进行比较。为了性能,系统估计了诊断准确性、精度和F1得分 metrics。最后,一些生成的图像被展示给一位专家进行评估,并看看专家能否准确地识别图像的类型和状态。专家认识到图像对于医学诊断非常有用,并根据形状和值确定严重程度等级,通过基于实际案例生成图像,可以用于健康和不健康患者之间的新不同的疾病阶段。
URL
https://arxiv.org/abs/2301.11871
