Deep-learning models in medical image analysis: Detection of esophagitis from the Kvasir Dataset
It addresses the problem of early detection of esophagitis, which can progress to cancer if untreated, by benchmarking deep learning models on a medical image dataset, but it is incremental as it compares existing models without introducing new methods.
This study compared the performance of several convolutional neural network models (GoogLeNet, ResNet-50, MobileNet V2, and MobileNet V3) in detecting esophagitis from the Kvasir dataset of endoscopic images, finding that GoogLeNet achieved the highest F1-scores and MobileNet V3 predicted esophagitis more confidently based on average true positive rate.
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.