Barrett's esophagus figured a swift rise in the number of cases in the past years. Although traditional diagnosis methods offered a vital role in early-stage treatment, they are generally time- and resource-consuming. In this context, computer-aided approaches for automatic diagnosis emerged in the literature since early detection is intrinsically related to remission probabilities. However, they still suffer from drawbacks because of the lack of available data for machine learning purposes, thus implying reduced recognition rates. This work introduces Generative Adversarial Networks to generate high-quality endoscopic images, thereby identifying Barrett's esophagus and adenocarcinoma more precisely. Further, Convolution Neural Networks are used for feature extraction and classification purposes. The proposed approach is validated over two datasets of endoscopic images, with the experiments conducted over the full and patch-split images. The application of Deep Convolutional Generative Adversarial Networks for the data augmentation step and LeNet-5 and AlexNet for the classification step allowed us to validate the proposed methodology over an extensive set of datasets (based on original and augmented sets), reaching results of 90% of accuracy for the patch-based approach and 85% for the image-based approach. Both results are based on augmented datasets and are statistically different from the ones obtained in the original datasets of the same kind. Moreover, the impact of data augmentation was evaluated in the context of image description and classification, and the results obtained using synthetic images outperformed the ones over the original datasets, as well as other recent approaches from the literature. Such results suggest promising insights related to the importance of proper data for the accurate classification concerning computer-assisted Barrett's esophagus and adenocarcinoma detection.

在过去几年中，巴雷特食管的病例数迅速增加。尽管传统的诊断方法在早期治疗中起着至关重要的作用，但它们通常会浪费时间和资源。在这种情况下，计算机辅助自动诊断方法在文献中应运而生，因为早期检测本质上与缓解可能性相关。然而，由于缺乏用于机器学习目的的可用数据，它们仍然遭受缺点，从而意味着降低了识别率。这项工作引入了生成对抗网络，以生成高质量的内窥镜图像，从而更精确地识别巴雷特的食道和腺癌。此外，卷积神经网络用于特征提取和分类目的。该方法在两个内窥镜图像数据集上得到了验证，并在完整图像和斑块分割图像上进行了实验。深度卷积生成对抗网络在数据扩充步骤中的应用以及LeNet-5和AlexNet在分类步骤中的应用，使我们能够在广泛的数据集集（基于原始集和扩充集）上验证所提出的方法，结果达到90％基于补丁的方法的准确性为85％，基于图像的方法的准确性为85％。两种结果均基于扩充数据集，并且在统计上与同类原始数据集所获得的结果不同。此外，在图像描述和分类的背景下评估了数据增强的影响，并且使用合成图像获得的结果优于原始数据集以及其他文献中的最新方法。这样的结果表明，关于正确数据对计算机辅助Barrett食管和腺癌检测的准确分类的重要性的前景令人鼓舞。