Deep-learning models in medical image analysis: Detection of esophagitis from the Kvasir Dataset

TL;DR

This study compares deep learning models for detecting esophagitis from endoscopic images, highlighting the performance differences among CNN architectures and their interpretability methods.

Contribution

It provides a comparative analysis of multiple CNN models on the Kvasir dataset for esophagitis detection, including interpretability assessments.

Findings

GoogLeNet achieved the highest F1-score

MobileNet V3 predicted esophagitis more confidently

Interpretability methods helped understand model decisions

Abstract

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.