Gastrointestinal Image Classification based on Convolutional Neural Network

被引：0

作者：

Wang, Shuo ^{[1
]}

Gao, Pengfei ^{[1
]}

Peng, Hui ^{[2
]}

机构：

[1] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB, Canada

[2] Huazhong Agr Univ, Coll Informat, Wuhan, Peoples R China

来源：

2021 8TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS RESEARCH AND APPLICATIONS, ICBRA 2021 | 2021年

关键词：

GI tract image; ROI extraction; Data augmentation; Transfer learning; Attention mechanism;

D O I：

10.1145/3487027.3487034

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The intelligent gastrointestinal image classification based on computer-aided diagnosis not only alleviates the shortage of endoscopist's missed diagnosis and misdiagnosis but also reduces the heavy diagnostic tasks to help prevent the deterioration of gastric diseases into gastric cancer. In our research work, we propose to use the Inception-Resnet-v2 with attention mechanism based on transfer learning to predict three-class anomalies of the gastrointestinal endoscopic imagery. Our model achieves a promising classification performance with 92.5% accuracy, 98.46% precision and 99.89% recall.

引用

页码：42 / 48

页数：7

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