Small Intestinal Polyp Detection in Wireless Capsule Endoscopy Images
Fan Shanhui1, Liu Shichen1, Cao E1, Fan Yihong2, Wei Kaihua1, Li Lihua1*
1(College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, China) 2(Department of Gastroenterology, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou 310006, China)
Abstract：Polyp is one of the most common small intestinal diseases. Wireless capsule endoscopy (WCE) is one of the routine clinical methods used for intestinal disease diagnosis. WCE can produce a mass of images during one examination, which may only contain a few abnormal images, therefore it is time consuming for doctors to review those images, which easily causes false detection and/or missed detection. Therefore, the development of an automatic polyp detection method is greatly valuable to provide support for doctors with better accuracy and efficiency. This study proposed a novel framework combining deep learning, transfer learning and data augmentation methods for polyp detection. The dataset used for model training and evaluation contained6 920 normal images and 6 864 polyp images, which was augmented from an original dataset containing 4 300 normal images and 429 polyp images. Specifically, three convolutional neural networks varied from depth to depth (AlexNet, VGGNet and GoogLeNet) were trained from scratch. The results showed that the GoogLeNet achieved the best performance with a sensitivity of 97.18%, specificity of 98.78% and accuracy of 97.99%. However, the training of deeper networks required more time and better computer, so we performed transfer learning strategy by fine-tuning a pretrained AlexNet. This model achieved a high accuracy of 97.74%, sensitivity of 96.57%, specificity of 98.89% and the area under the receiver operating characteristic curve (AUC) of 0.996. The proposed method provided an effective way for precise automatic intestinal polyp detection with limited training data, lower time cost and computer configuration, having potentials to help doctors efficiently detect intestinal polyp with WCE images.
范姗慧, 刘士臣, 曹鹗, 范一宏, 魏凯华, 厉力华. 无线胶囊内窥镜图像小肠息肉的自动识别[J]. 中国生物医学工程学报, 2019, 38(5): 522-532.
Fan Shanhui, Liu Shichen, Cao E, Fan Yihong, Wei Kaihua, Li Lihua. Small Intestinal Polyp Detection in Wireless Capsule Endoscopy Images. Chinese Journal of Biomedical Engineering, 2019, 38(5): 522-532.
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