文章基本信息
- 标题：Gastrointestinal polyp detection through a fusion of contourlet transform and Neural features
- 本地全文：下载
- 作者：Mahmodul Hasan ; Nazrul Islam ; Mohammad Motiur Rahman 等
- 期刊名称：Journal of King Saud University @?C Computer and Information Sciences
- 印刷版ISSN：1319-1578
- 出版年度：2020
- 页码：1-8
- DOI：10.1016/j.jksuci.2019.12.013
- 出版社：Elsevier
- 摘要：The gastrointestinal polyp (GIP) is the abnormal growth of tissues in digestive organs. Identifying these polyps from endoscopy video or image is a tremendous task to reduce the future risk of gastrointestinal cancer. This paper proposes a proper diagnosis method of polyp using a fusion of contourlet transform and fine-tuned VGG19 pre-trained model from enhanced endoscopic 224 × 224 patch images. This study has used different fine-tuned models (Alexnet, ResNet50, VGG16, VGG19) as well as a few scratch models while fine-tuned VGG19 works better. Also, this research has used Principal Component Analysis (PCA) and Minimum Redundancy Maximum Relevance (MRMR) dimensionality reduction methods to collect the intuitive features for classification. In Support Vector Machine (SVM) based polyp detection, the prior method (PCA) performs better. Besides, a proposed algorithm marks polyp region from identified polyp patches and uses a binning strategy to process video. A set of experiments are performed on standard public data sets and found comparative improved performance with an accuracy of 99.59%, sensitivity of 99.74% and specificity of 99.44%. This work can be instrumental for the radiologist for diagnosis of polyps during real-time endoscopy. Recommended articles Citing articles (0) Peer review under responsibility of King Saud University. View Abstract © 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. Recommended articles No articles found. Citing articles Article Metrics View article metrics About ScienceDirect Remote access Shopping cart Advertise Contact and support Terms and conditions Privacy policy We use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies . Copyright © 2020 Elsevier B.V. or its licensors or contributors. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V.
