Small bowel motility assessment based on fully convolutional networks and long short-term memory

Pei, Mengqi; Wu, Xing; Guo, Yike; Fujita, Hamido

doi:10.1016/j.knosys.2017.01.023

Cited by 31 publications

(11 citation statements)

References 16 publications

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“…More importantly, the database used in the present study contained not only instances of ulcers but also several other types of abnormalities, including for example, vascular lesions, for which texture may not be as discriminative as color [11]. In terms of the use of deep learning networks, previous studies, including Seguia et al [35] used their designed CNN in the analysis of WCE images. Their CNN achieved 96% accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…The evolution of deep learning provided new opportunities to improve the analysis of WCE images [10,32,33,34,35]. A review of the literature shows that deep learning has proved to be more successful than traditional machine learning tools.…”

Section: Wireless Capsule Endoscopy Image Ulcer Detection Techniquesmentioning

confidence: 99%

“…Their experiment achieved a F-measure score of 99%. Pei et al [35] have also used fully convolutional networks (FCNs) with long short-term memory (LSTM). The FCN-LSTM was trained on five cine MRI sequences that had no labeling, and the FCN network was trained on a large dataset of 50 raw cine MRI sequences, which were labeled.…”

Section: Wireless Capsule Endoscopy Image Ulcer Detection Techniquesmentioning

confidence: 99%

“…Segu et al [35] used a computer aided decision system based on a generic feature-learning approach for WCE. They built a large dataset of 120,000 labeled WCE images to train the CNN.…”

Section: Wireless Capsule Endoscopy Image Ulcer Detection Techniquesmentioning

confidence: 99%

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Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images

Alaskar

Hussain

Alaseem

et al. 2019

Sensors

143

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Wireless Capsule Endoscopy Image Ulcer Detection Techniquesmentioning

confidence: 99%

Section: Wireless Capsule Endoscopy Image Ulcer Detection Techniquesmentioning

confidence: 99%

“…Segu et al [35] used a computer aided decision system based on a generic feature-learning approach for WCE. They built a large dataset of 120,000 labeled WCE images to train the CNN.…”

Section: Wireless Capsule Endoscopy Image Ulcer Detection Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images

Alaskar

Hussain

Alaseem

et al. 2019

Sensors

143

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“…The authors in Yuan & Meng (2017) suggested the use of an image manifold with stacked sparse auto-encoder to recognize polyps in endoscopic images. Instead, the authors in Pei et al (2017) proposed a CADx system to recognize and assess the small bowel using features extracted from long short-term memory (LSTM).…”

Section: Introductionmentioning

confidence: 99%

GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases

Attallah¹,

Sharkas²

2021

PeerJ Computer Science

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Gastrointestinal (GI) diseases are common illnesses that affect the GI tract. Diagnosing these GI diseases is quite expensive, complicated, and challenging. A computer-aided diagnosis (CADx) system based on deep learning (DL) techniques could considerably lower the examination cost processes and increase the speed and quality of diagnosis. Therefore, this article proposes a CADx system called Gastro-CADx to classify several GI diseases using DL techniques. Gastro-CADx involves three progressive stages. Initially, four different CNNs are used as feature extractors to extract spatial features. Most of the related work based on DL approaches extracted spatial features only. However, in the following phase of Gastro-CADx, features extracted in the first stage are applied to the discrete wavelet transform (DWT) and the discrete cosine transform (DCT). DCT and DWT are used to extract temporal-frequency and spatial-frequency features. Additionally, a feature reduction procedure is performed in this stage. Finally, in the third stage of the Gastro-CADx, several combinations of features are fused in a concatenated manner to inspect the effect of feature combination on the output results of the CADx and select the best-fused feature set. Two datasets referred to as Dataset I and II are utilized to evaluate the performance of Gastro-CADx. Results indicated that Gastro-CADx has achieved an accuracy of 97.3% and 99.7% for Dataset I and II respectively. The results were compared with recent related works. The comparison showed that the proposed approach is capable of classifying GI diseases with higher accuracy compared to other work. Thus, it can be used to reduce medical complications, death-rates, in addition to the cost of treatment. It can also help gastroenterologists in producing more accurate diagnosis while lowering inspection time.

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