Abstract
Gastric cancer has a high mortality rate worldwide, but it can be prevented with early detection through regular gastroscopy. Herein, we propose a deep learning-based computer-aided diagnosis (CADx) system applying data augmentation to help doctors classify gastroscopy images as normal or abnormal. To improve the performance of deep learning, a large amount of training data are required. However, the collection of medical data, owing to their nature, is highly expensive and time consuming. Therefore, data were generated through deep convolutional generative adversarial networks (DCGAN), and 25 augmentation policies optimized for the CIFAR-10 dataset were implemented through AutoAugment to augment the data. Accordingly, a gastroscopy image was augmented, only high-quality images were selected through an image quality-measurement method, and gastroscopy images were classified as normal or abnormal through the Xception network. We compared the performances of the original training dataset, which did not improve, the dataset generated through the DCGAN, the dataset augmented through the augmentation policies of CIFAR-10, and the dataset combining the two methods. The dataset combining the two methods delivered the best performance in terms of accuracy (0.851) and achieved an improvement of 0.06 over the original training dataset. We confirmed that augmenting data through the DCGAN and CIFAR-10 augmentation policies is most suitable for the classification model for normal and abnormal gastric endoscopy images. The proposed method not only solves the medical-data problem but also improves the accuracy of gastric disease diagnosis.
Highlights
According to the statistics released by the Global Cancer Observatory in 2018, gastric cancer is the fifth most frequently diagnosed cancer and the third leading cause of cancer deaths worldwide, as shown in Figure 1 [1].To increase the survival rate of patients with gastric cancer, it is important to detect and treat it early through gastroscopy
We propose a classification method for normal and abnormal gastroscopy images through CADx
The data required for learning were secured by applying an imagegeneration method through the deep convolutional generative adversarial networks (DCGAN) and an automated augmentation method using a CNN and recurrent neural network (RNN)
Summary
To increase the survival rate of patients with gastric cancer, it is important to detect and treat it early through gastroscopy. A previous study showed that the survival rate of patients with gastric cancer who underwent gastric endoscopy was 2.24 times higher than that of those who did not [2]. Precancerous lesions that cause gastric cancer include gastritis, gastric ulcer, and gastric bleeding. Most of these gastric diseases are difficult to detect because they are asymptomatic until they develop into gastric cancer. Gastric cancer can be prevented through the early detection of lesions that develop into gastric cancer with regular gastroscopy. As the imaging technology continually develops and the number of medical images rapidly increases, Appl.
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