文章基本信息

标题：Dense-Res Net for Endoscopic Image Classification
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作者：Quoc-Huy Trinh ; Minh-Van Nguyen
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2021
卷号：11
期号：11
语种：English
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：We propose a method that configures Fine-tuning to a combination of backbone DenseNet and ResNet to classify eight classes showing anatomical landmarks, pathological findings, to endoscopic procedures in the GI tract. Our Technique depends on Transfer Learning which combines two backbones, DenseNet 121 and ResNet 101, to improve the performance of Feature Extraction for classifying the target class. After experiment and evaluating our work, we get accuracy with an F1 score of approximately 0.93 while training 80000 and test 4000 images.
关键词：Kvasir dataset;dense-res;medical image;classification;deep neural network