<p>Digestive disorders can be signs of long-term conditions such as cancer, and as such, they should be treated seriously. Endoscopic exams of the gastrointestinal tract allow for the early detection of these conditions and facilitate effective treatment; these procedures have their effectiveness limited by variations in operator performance, due to human error. Support systems are desired to help specialists detect and diagnose pathologies in this type of exam. This work used a seldom utilized dataset, the ERS dataset, which contains 121,399 labeled images, to evaluate eight models from the EfficientNet family of architectures, as well as three models from the EfficientNetV2 iteration of this architecture, for the task of binary classification of endoscopic images. This work also compared their performance to four other widely used CNN architectures for the same task, along with the baseline results published by the authors of the dataset. Each model was evaluated in a 5-fold cross-validation procedure, following the same training protocol. The experiments have shown that the best-performing architecture was EfficientNetV2M, followed closely by EfficientNetB7, with the former achieving average accuracy and F1-Score values of, respectively, 82.24% and 88.15%. </p>