Research Center of Intelligent Medical Information Processing, School of Information Science and Engineering, Shandong University, Qingdao 266237, China.
Research Center of Intelligent Medical Information Processing, School of Information Science and Engineering, Shandong University, Qingdao 266237, China.
Comput Methods Programs Biomed. 2020 Apr;187:105019. doi: 10.1016/j.cmpb.2019.105019. Epub 2019 Aug 9.
Fungal keratitis is caused by inflammation of the cornea that results from infection by fungal organisms. The lack of an early effective diagnosis often results in serious complications even blindness. Confocal microscopy is one of the most effective methods in the diagnosis of fungal keratitis, but the diagnosis depends on the subjective judgment of medical experts.
To address this problem, this paper proposes a novel convolutional neural network framework for the automatic diagnosis of fungal keratitis using data augmentation and image fusion. Firstly, a normal image is augmented by flipping to solve the problem of having a limited and imbalanced database. Secondly, a sub-area contrast stretching algorithm is proposed for image preprocessing to highlight the key structures in the images and to filter out irrelevant information. Thirdly, the histogram matching fusion algorithm is implemented, then the preprocessed image is fused with the original image to form a new algorithm framework and a new database. Finally, the traditional convolutional neural network is integrated into the novel algorithm framework to perform the experiments.
Experiments show that the accuracy of traditional AlexNet and VGGNet is 99.35% and 99.14%, that of AlexNet and VGGNet based on MF fusion is 99.80% and 99.83%, and that of AlexNet and VGGNet based on histogram matching fusion (HMF) is 99.95% and 99.89%. The experimental results show that the AlexNet framework using data augmentation and image fusion achieves a perfect trade-off between the diagnostic performance and the computational complexity, with a diagnostic accuracy of 99.95%.
These experimental results demonstrate the novel convolutional neural network framework perfectly balances the diagnostic performance and computational complexity, and can improve the effect and real-time performance in the diagnosis of fungal keratitis.
真菌性角膜炎是由真菌感染引起的角膜炎症。由于缺乏早期有效的诊断，常导致严重的并发症，甚至失明。共焦显微镜检查是诊断真菌性角膜炎最有效的方法之一，但诊断结果取决于医学专家的主观判断。
针对这一问题，本文提出了一种利用数据增强和图像融合的新型卷积神经网络框架，用于真菌性角膜炎的自动诊断。首先，通过翻转来增强正常图像，解决了数据库有限且不平衡的问题。其次，提出了一种子区域对比度拉伸算法进行图像预处理，以突出图像中的关键结构，并滤除无关信息。然后，实现了直方图匹配融合算法，将预处理后的图像与原始图像进行融合，形成新的算法框架和新的数据库。最后，将传统卷积神经网络集成到新的算法框架中进行实验。
实验表明，传统的 AlexNet 和 VGGNet 的准确率分别为 99.35%和 99.14%，基于 MF 融合的 AlexNet 和 VGGNet 的准确率分别为 99.80%和 99.83%，基于直方图匹配融合（HMF）的 AlexNet 和 VGGNet 的准确率分别为 99.95%和 99.89%。实验结果表明，基于数据增强和图像融合的 AlexNet 框架在诊断性能和计算复杂度之间实现了完美的权衡，诊断准确率达到 99.95%。
这些实验结果表明，该新型卷积神经网络框架完美地平衡了诊断性能和计算复杂度，可以提高真菌性角膜炎诊断的效果和实时性。
