The researchers studied how different types of training data affect the accuracy of deep learning models in predicting early gastric cancer during endoscopy. They collected images of cancerous and non-cancerous lesions and trained three models with varying levels of diversity in the training data. The model trained with the most diverse data performed the best in accurately identifying cancerous lesions. This suggests that using a wide range of images for training deep learning models can improve their ability to detect early gastric cancer.