Automatic Polyp Detection from Endoscope Image using Transfer Learning

Abstract

Deep learning technology becomes popular to realize automatic polyp detection in the medical diagnosis. This paper proposes a method using transfer learning applied for the endoscope image by introducing fine turning with CNN which gives the high evaluation in the general object recognition. Proposed method uses DenseNet , AlexNet and VGG16 models and evaluates the accuracy to each model which is learned with ImageNet, and constructed effective classifiers for polyp detection. Experimental result suggests that the proposed method improves the classification accuracy using CNN feature and SVM.