Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network. (April 2020) - Liu, Zhi; Cao, Yankun; Li, Yujun; Xiao, Xiaoyan; Qiu, Qingchen; Yang, Meijun; Zhao, Yuefeng; Cui, Lizhen
Highlights: A novel CNN framework for the automatic diagnosis of fungal keratitis is proposed. A normal image is augmented by flipping. A sub-area contrast stretching algorithm is proposed for image preprocessing. A histogram matching fusion algorithm is implemented and new databases are formed. The traditional CNN is integrated into the novel algorithm framework. Abstract: Background and objectives: 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. Methods: 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. Results: 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%. Conclusions: 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.