  Early detection of esophagitis is important because this condition can
progress to cancer if left untreated. However, the accuracies of different deep
learning models in detecting esophagitis have yet to be compared. Thus, this
study aimed to compare the accuracies of convolutional neural network models
(GoogLeNet, ResNet-50, MobileNet V2, and MobileNet V3) in detecting esophagitis
from the open Kvasir dataset of endoscopic images. Results showed that among
the models, GoogLeNet achieved the highest F1-scores. Based on the average of
true positive rate, MobileNet V3 predicted esophagitis more confidently than
the other models. The results obtained using the models were also compared with
those obtained using SHapley Additive exPlanations and Gradient-weighted Class
Activation Mapping.
