Title
A Locally-Processed Light-Weight Deep Neural Network For Detecting Colorectal Polyps In Wireless Capsule Endoscopes
Abstract
Wireless capsule endoscopes (WCE) are revolutionary devices for noninvasive inspection of gastrointestinal tract diseases. However, it is tedious and error-prone for physicians to inspect the huge number of captured images. Artificial Intelligence supports computer- aided diagnostic tools to tackle this challenge. Unlike previous research focusing on the application of large deep neural network (DNN) models for processing images that have been saved on the computer, we propose a lightweight DNN model that has the potential of running locally in the WCE. Thus, only images with diseases are transmitted, saving energy on data transmission. Several aspects of the design are presented in detail, including the DNN's architecture, the loss function, the criterion of true positive, and data augmentation. We explore design parameters of the DNN architecture in several experiments. These experiments use a training dataset of 1222 images and a test dataset with 153 images. The results of our study indicate that our designed DNN has an Average Precision of AP(25) = 91.7% on our test dataset while the parameter storage size is only 29.1KB, which is small enough to run locally on a WCE. In addition, the real-time performance of the designed DNN model is tested on an FPGA, completing one image classification in less than 6.28 ms, which is much less than the 167 ms needed to achieve real-time operation on the WCE. We conclude that our DNN model possesses significant advantages over previous models for WCEs, in terms of model size and real-time performance.
Year
DOI
Venue
2021
10.1007/s11554-021-01126-7
JOURNAL OF REAL-TIME IMAGE PROCESSING
Keywords
DocType
Volume
Medical image processing, Deep learning, Polyp detection, Wireless capsule endoscope (WCE)
Journal
18
Issue
ISSN
Citations 
4
1861-8200
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Yunlong Wang100.34
Sunyoung Yoo200.34
Jan-Matthias Braun300.34
Esmaeil S. Nadimi495.90