Transfer Learning with Convolutional Neural Network for Early Gastric Cancer Classification on Magnifiying Narrow-Band Imaging Images

Immediate diagnosis and treatment of early gastric cancer (EGC) can efficiently improve the survival of gastric cancer (GC). Magnification endoscopy with narrow-band imaging (M-NBI) as a kind of main tool is widely applied in hospital to detect EGC by illustrating abnormal vascellum morphologies. In this paper, transfer learning by fine-tuning deep convolutional neural networks (CNNs) is applied to automatically classify M-NBI images into two groups: normal gastric images and EGC images. Moreover, this paper explores how transfer learning affects the classification performance from four aspects: training dataset, basic architectures of the deep CNN, the number of fine-tuned network layers and size of the network input image; this paper also gives some guidances for later researches in this area. VGG16, InceptionV3 and InceptionResNetV2 are selected to help us accomplish the M - NBI image classification task. Experimental results show that transfer learning of deep CNN features performs better than traditional handcraft methods. And the top accuracy, sensitivity and specificity are 0.985, 0.981 and 0.989, respectively, obtained by fine-tuning Inception V3.