文章基本信息

标题：Deep Learning Models for Colorectal Polyps
本地全文：下载
作者：Ornela Bardhi ; Daniel Sierra-Sosa ; Begonya Garcia-Zapirain 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2021
卷号：12
期号：6
页码：245
DOI：10.3390/info12060245
出版社：MDPI Publishing
摘要：Colorectal cancer is one of the main causes of cancer incident cases and cancer deaths worldwide. Undetected colon polyps, be them benign or malignant, lead to late diagnosis of colorectal cancer. Computer aided devices have helped to decrease the polyp miss rate. The application of deep learning algorithms and techniques has escalated during this last decade. Many scientific studies are published to detect, localize, and classify colon polyps. We present here a brief review of the latest published studies. We compare the accuracy of these studies with our results obtained from training and testing three independent datasets using a convolutional neural network and autoencoder model. A train, validate and test split was performed for each dataset, 75%, 15%, and 15%, respectively. An accuracy of 0.937 was achieved for CVC-ColonDB, 0.951 for CVC-ClinicDB, and 0.967 for ETIS-LaribPolypDB. Our results suggest slight improvements compared to the algorithms used to date.
关键词：colon cancer; deep learning; detection; classification; localization; CNN; autoencoders colon cancer ; deep learning ; detection ; classification ; localization ; CNN ; autoencoders