Precancerous Diseases Classification Based on Fusion of Shallow and Deep Features
Pan Yanqi1, Chen Rui1, Zhang Xu1, Zhang Xinsen1, Liu Jiquan1*, Hu Weiling2*, Duan Huilong1#, Si Jianmin2
1(College of Biomedical Engineering & Instrument Science,Zhejiang University,Hangzhou 310000,China) 2(Department of Gastroenterology,Sir Run Run Shaw Hospital Zhejiang University School of Medicine,Hangzhou 310000,China)
Abstract Precancerous disease recognition is of great significance in reducing the risk of gastric cancer. This paper proposed a method for identifying precancerous diseases based on the fusion of shallow and deep features of gastroscopic images. Firstly,according to properties of gastric images,75-dimensional shallow features were designed manually,including histogram features,texture features and higher order features. Secondly,based on the networks of Resnet and GoogLeNet,we added a fully connected layer before the output layer to extract the deep features of the images. To ensure consistent feature weights,the dimension of the added fully connected layer was 75. Finally,the shallow features were merged with deep features. Machine learning classifiers were used to identify three types of precancerous diseases,namely gastric polyps,gastric ulcers and gastric erosions. We collected 380 images for each disease,and 75% were used as training sets,the others were used as testing sets. We conducted experiments using traditional machine learning,deep learning and feature fusion proposed in this paper. Experiment results showed that the recognition accuracy of the feature fusion method proposed was as high as 95.18%,significantly better than that of traditional machine learning methods (74.12%) and deep learning methods (92.54%). This proposed method made full use of the shallow features and deep features to provide clinical decision support for doctors and assist in the diagnosis of precancerous diseases.
Pan Yanqi,Chen Rui,Zhang Xu, et al. Precancerous Diseases Classification Based on Fusion of Shallow and Deep Features[J]. Chinese Journal of Biomedical Engineering, 2020, 39(4): 413-421.
[1] Stewart B,Wild CP.World cancer report 2014[M].Lyon:International Agency for Research on Cancer,2015:544-559. [2] Isobe Y,Nashimoto A,Akazawa K,et al.Gastric cancer treatment in Japan:2008 annual report of the JGCA nationwide registry[J].Gastric Cancer,2011,14(4):301-316. [3] 左婷婷,郑荣寿,曾红梅,等.中国胃癌流行病学现状[J].中国肿瘤临床,2017,44(1):52-58. [4] Orditura M,Galizia G,Sforza V,et al.Treatment of gastric cancer[J].World Journal of Gastroenterology,2014,20(7):1635-1649. [5] Shin WG,Kim HU,Song HJ,et al.Surveillance strategy of atrophic gastritis and intestinal metaplasia in a country with a high prevalence of gastric cancer[J].Dig Dis Sci,2012,57(3):746-752. [6] 房静远,刘文忠,李兆申,等.中国慢性胃炎共识意见[J].胃肠病学,2013,18(1):24-36. [7] Fox JG,Wang TC.Inflammation,atrophy,and gastric cancer[J].The Journal of Clinical Investigation,2007,117(1):60-69. [8] Shin HC,Roth HR,Gao M,et al.Deep convolutional neural networks for computer-aided detection:CNN architectures,dataset characteristics and transfer learning[J].IEEE Trans Med Imaging,2016,35(5):1285-1298. [9] Anthimopoulos M,Christodoulidis S,Ebner L,et al.Lung pattern classification for interstitial lung diseases using a deep convolutional neural network[J].IEEE Trans Med Imaging,2016,35(5):1207-1216. [10] Kamnitsas K,Ledig C,Newcombe VF,et al.Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation[J].Med Image Anal,2017,36(2):61-78. [11] Zhang Xu,Hu Weiling,Chen Fei,et al.Gastric precancerous diseases classification using CNN with a concise model[J].PLoS ONE,2017,12(9):1-10. [12] Zhang Xu,Chen Fei,Yu Tao,et al.Real-time gastric polyp detection using convolutional neural networks[J].PLoS ONE,2019,14(3):1-16. [13] Zhang Ruikai,Zheng Yali,Mak TWC,et al.Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domain[J].IEEE journal of biomedical and health informatics,2017,21(1):41-47. [14] Sanchez-Gonzalez A,Garcia-Zapirain B,Sierra-Sosa D,et al.Automatized colon polyp segmentation via contour region analysis[J].Comput Biol Med,2018,100(9):152-164. [15] Hiroshi Y,Taichi S,Tomoharu K,et al.Automated histological classification of whole-slide images of gastric biopsy specimens[J].Gastric Cancer,2018,21(2):249-257. [16] Sharma H,Zerbe N,Klempert I,et al.Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology[J].Comput Med Imaging Graph,2017,61(11):2-13. [17] Hirasawa T,Aoyama K,Tanimoto T,et al.Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images[J].Gastric Cancer,2018,21(4):653-660. [18] 洪继光.灰度-梯度共生矩阵纹理分析方法[J].自动化学报,1984,10(1):22-25. [19] 耿庆田,赵宏伟.基于分形维数和隐马尔科夫特征的车牌识别[J].光学精密工程,2013,21(12):3198-3204. [20] 戴光,崔巍,张颖,等.基于灰度-梯度共生矩阵的焊缝缺陷聚类分析[J].中国安全科学学报,2013,23(3):79-85. [21] 于俊清,吴泽斌,吴飞,等.多媒体工程:2016——图像检索研究进展与发展趋势[J].中国图象图形学报,2017,22(11):1467-1485. [22] Szegedy C,Wei L,Yangqing J,et al.Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Las Vegas:IEEE,2015:1-9. [23] He K,Zhang X,Ren S,et al.Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Las Vegas:IEEE,2016:770-778. [24] Xie S,Girshick R,Dollár P,et al.Aggregated Residual Transformations for Deep Neural Networks[C]//proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Honolulu:IEEE,2017:5987-5995. [25] 中国电子技术标准化研究院.人工智能标准化白皮书[M].北京:中国电子技术标准化研究院,2018:1-102. [26] Goodfellow I,Bengio Y,Courville A.深度学习[M].北京:人民邮电出版社,2017:96-97. [27] Jolliffe IT,Cadima J.Principal component analysis:a review and recent developments[J].Philosophical Transactions of the Royal Society A:Mathematical,Physical and Engineering Sciences,2016,374(2065):1-16.
