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Design and implementation of deep learning algorithm for image discrimination of light field confocal endoscopy for esophageal cancer and gastric cancer

The survival rate of early gastric cancer and esophageal cancer is more than 90%. Confocal endoscopy can detect cell morphology and mucosal glandular structure (depth about 500 μm), presenting a cross-sectional microscopic image unfamiliar to both endoscopists and pathologists. Therefore, using computer-aided diagnosis technology to complete real-time artificial intelligence diagnosis of early esophageal and gastric cancer is of great significance for early detection and early treatment of cancer patients. ResNet convolution neural network based image classification model, the introduction of attention mechanism based on CBAM module to improve the performance of the model, to achieve intelligent diagnosis of confocal endoscopy images.