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Alaskar, H, Hussain, A, Al-Aseem, N, Liatsis, P and Al-Jumeily, D (2019) Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images. Sensors, 19 (6). ISSN 1424-8220
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Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and subsequent classification. This research proposes an automated system for detection and classification of ulcers in WCE images, based on state-of-the-art deep learning networks. Deep learning techniques, and in particular, convolutional neural networks (CNNs), have recently become popular in the analysis and recognition of medical images. The medical image datasets used in this study were obtained from WCE video frames. In this work, two milestone CNN architectures, namely the AlexNet and the GoogLeNet are extensively evaluated in object classification into ulcer or non-ulcer. Furthermore, we examine and analyze the images identified as containing ulcer objects to evaluate the efficiency of the utilized CNNs. Extensive experiments show that CNNs deliver superior performance, surpassing traditional machine learning methods by large margins, which supports their effectiveness as automated diagnosis tools.
| Item Type: | Article |
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| Uncontrolled Keywords: | 0301 Analytical Chemistry, 0906 Electrical and Electronic Engineering |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) |
| Divisions: | Computer Science & Mathematics |
| Publisher: | MDPI |
| Related URLs: | |
| Date Deposited: | 09 Apr 2019 09:54 |
| Last Modified: | 04 Sep 2021 01:51 |
| DOI or ID number: | 10.3390/s19061265 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/10524 |
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