Deep-learning models in medical image analysis: Detection of esophagitis from the Kvasir Dataset
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Medical Image AnalysisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Kvasir-VQA: A Text-Image Pair GI Tract Dataset
We introduce Kvasir-VQA, an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations to facilitate advanced machine learning tasks in Gastrointestinal (…
Image CaptioningImage GenerationMedical Image AnalysisMedical Image Generation+6Abnormalities and Disease Detection in Gastro-Intestinal Tract Images
Gastrointestinal (GI) tract image analysis plays a crucial role in medical diagnosis. This research addresses the challenge of accurately classifying and segmenting GI images for real-time applications, where traditional…
Medical DiagnosisKvasir-SEG: A Segmented Polyp Dataset
Pixel-wise image segmentation is a highly demanding task in medical-image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present Kvasir-SE…
Image SegmentationMedical Image AnalysisMedical Image SegmentationPolyp Segmentation+2Self-supervised Learning for Gastrointestinal Pathologies Endoscopy Image Classification with Triplet Loss
Recently, the amount of GI tract datasets is introduced more and more by gathering from contests and challenges. The most common task needs to solve that is to classify images from the GI tract into various classes. Howe…
image-classificationImage ClassificationSelf-Supervised LearningTripletYOLO-MED : Multi-Task Interaction Network for Biomedical Images
Object detection and semantic segmentation are pivotal components in biomedical image analysis. Current single-task networks exhibit promising outcomes in both detection and segmentation tasks. Multi-task networks have g…
object-detectionObject DetectionSegmentationSemantic Segmentation