Shuo Wang
ABSTRACT
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.
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