# Generative Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease
## 3 Results
### Gan  
This section summarizes original studies with augmentation, classification, detection, generation and segmentation based on the GAN. As reported above, different GANs would be appropriate for different tasks for the early diagnosis of GID; i.e., patch GAN (accuracy 91.9%) in the case of classification [21], bi-directional cycle GAN (structural similarity 98.8%) in the case of data generation [30] and semi-supervised GAN (Dice 89.4%) in the case of segmentation [27]. Here, the GAN improves the performance of a traditional classification, detection or segmentation model by (1) augmenting data or (2) presenting a feedback loop between the generative model and the discriminative model. It is to be reminded that the GAN generates image data by training the generative model vs. the discriminative model. The generative model strives to generate fake images from random noises so as to fool the discriminative model. The discriminative model strives to discriminate fake images from real images (input images) so as to outwit the generative model [16]. The original GAN maps an input image into a single probability value, while the patch GAN maps an input image into multiple probability values of multiple patches [21]. The bi-directional cycle GAN uses both mappings (1) and (2): (1) mapping a blurred input into a single probability value for a clear sample; and (2) mapping a clear input into a single probability value for a blurred sample. On the other hand, the unidirectional cycle GAN employs either of mappings (1) and (2) [30]. The semi-supervised GAN uses both unlabeled and labeled data to improve its performance [27].  
The aim of a recent study with the patch GAN was to develop and validate deep-learning classification models for gastric cancer [21]. Data came from 630 images at a general hospital. The 100 images were taken as the test set. Then, the 530 images were augmented to 9360 images, which were split into the training and validation sets with a 95:05 ratio (8892468 images). A major criterion for the test of the trained and validated models was accuracy. The patch GAN and the endoscopist (baseline) were trained, validated, tested and compared. Based on the results of this study, the accuracy of the patch GAN was higher than that of the endoscopist: 91.9% vs. 83.6%. Another recent study used 1290 images and the cycle GAN with bi-direction for the generation of GID images [30]. The cycle GAN with bi-direction and its unidirectional counterpart (baseline) were trained, validated, tested and compared. According to the findings of this study, the structural similarity of the cycle GAN with bi-direction was higher than that of its unidirectional counterpart: 98.8% vs. 96.8%. The purpose of another recent study with the semi-supervised GAN was to develop and validate deep learning segmentation models for GID [27]. The source of data was 4880 images at a general hospital and from public sources (e.g., CVC-ClinicDB, ETIS-LaribPolypDB, Kvasir-SEG). A major criterion for the test of the trained and validated models was Dice. The semi-supervised GAN and its supervised counterpart (baseline) were trained, validated, tested and compared. The DICE of the patch GAN was found to be higher than that of its supervised counterpart: 89.4% vs. 82.1%.