Bibliographic Details
| Title: |
Deep-learning models in medical image analysis: Detection of esophagitis from the Kvasir Dataset |
| Authors: |
Yoshiok, Kyoka, Tanioka, Kensuke, Hiwa, Satoru, Hiroyasu, Tomoyuki |
| Publication Year: |
2023 |
| Collection: |
Computer Science |
| Subject Terms: |
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition |
| More Details: |
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. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2301.02390 |
| Accession Number: |
edsarx.2301.02390 |
| Database: |
arXiv |
