Enhanced Ulcer Recognition from Capsule Endoscopic Images Using Texture Analysis

Charisis, Vasileios; Hadjileontiadis, Leontios; Sergiadis, George D.

doi:10.5772/32940

Cited by 13 publications

(9 citation statements)

References 58 publications

(44 reference statements)

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“…Ulcers are one of the most common lesions in the GI tract; an estimated 1 out of every 10 persons is believed to suffer from ulcers [13]. An ulcer is defined as an area of tissues destroyed by gastric juice and showing a discontinuity or break in a bodily membrane [9, 11].…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization

Wang

Xing

Zhang

et al. 2019

Computational and Mathematical Methods in Medicine

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Wireless capsule endoscopy (WCE) has developed rapidly over the last several years and now enables physicians to examine the gastrointestinal tract without surgical operation. However, a large number of images must be analyzed to obtain a diagnosis. Deep convolutional neural networks (CNNs) have demonstrated impressive performance in different computer vision tasks. Thus, in this work, we aim to explore the feasibility of deep learning for ulcer recognition and optimize a CNN-based ulcer recognition architecture for WCE images. By analyzing the ulcer recognition task and characteristics of classic deep learning networks, we propose a HAnet architecture that uses ResNet-34 as the base network and fuses hyper features from the shallow layer with deep features in deeper layers to provide final diagnostic decisions. 1,416 independent WCE videos are collected for this study. The overall test accuracy of our HAnet is 92.05%, and its sensitivity and specificity are 91.64% and 92.42%, respectively. According to our comparisons of F1, F2, and ROC-AUC, the proposed method performs better than several off-the-shelf CNN models, including VGG, DenseNet, and Inception-ResNet-v2, and classical machine learning methods with handcrafted features for WCE image classification. Overall, this study demonstrates that recognizing ulcers in WCE images via the deep CNN method is feasible and could help reduce the tedious image reading work of physicians. Moreover, our HAnet architecture tailored for this problem gives a fine choice for the design of network structure.

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization

Wang

Xing

Zhang

et al. 2019

Computational and Mathematical Methods in Medicine

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“…Nonetheless, such results can be viewed with caution owing to the lead time bias phenomenon [16,17]. The lifespan of the cancer would be higher when screening or testing indicates a pre-symptomatic cancer, particularly through the early detection which has not been successful, despite the onset of signs at a XXXXX: Deep learning based earlier detection of esophageal cancer using improved empirical wavelet transform from endoscopic image (XXXXX) VOLUME XX, 2019 2 later date [18][19][20][21][22]. Figure 1 shows the Esophageal Cancer occurrence.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Earlier Detection of Esophageal Cancer Using Improved Empirical Wavelet Transform From Endoscopic Image

Xue

Wei

et al. 2020

IEEE Access

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In the current scenario, the research perspective on esophageal cancer becomes severe, high-prognosis malignancy; poor prognosis is primarily attributed to the fact that most tumors remain asymptomatic and unrelated before it grows through the esophagus. Significant decreases in mortality from esophageal cancer may require effective approaches to detect and nurse more patients at early, curable stages. A new Improved Empirical Wavelet Transform (IEWT) dependent on feature extraction approach and a consistent homology for the diagnosis of early esophageal endoscopic cancer have been proposed in this article. The approach is to convert an input endoscope image into CIE colored spaces L * x * y , and the x * and y * components to create a fusion image for analysis. Further, the two kinds of wavelets are obtained by adding the two forms to the fusion signal. Another is the lower-frequency component provided by the improved empirical wavelet transformation of the wave, and the other is the high-components generated from the Deep Learning-based Complex Empirical Wavelet Transformation (DL-CEWT). The fractal sizes are determined using the box interpolation method for each small block, and the abnormal regions are defined for the basis of their fractal sizes. Binary pictures are obtained by the complex threshold in each frequency variable and then divided into small blocks in every binary image. Using the homology of every block to obtain the new features in the entry image. The extraction strategies for this application are comprehensive and preliminary findings indicate that the method is effective for the early detection of esophageal cancer in an image.

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“…In decade ago, conventionally endoscopic technique is used for examination of upper and lowers a part of GI track. Gastroenterologists face challenges to examine the whole intestine, in particular pose [1]. The length of human intestine is about 20feet and it having several windings and it creates the examination is really painful and complicated.…”

Section: Introductionmentioning

confidence: 99%

“…Recorder system is ware by patient. This process is approximately 8 hours or till battery exhausts [1]. Figure 1 show wireless capsule endoscopy.…”

Section: Introductionmentioning

confidence: 99%

A Novel Strategy to Image Decomposition for Wireless Capsule Endoscopy Images

Prashant M.*,

Vishwanath R.,

Shrinivas A.

2020

IJITEE

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Now a day wireless capsule endoscopy (WCE) is broadly used for detection of gastro internal organ diseases. WCE is produces quite 55000 images but still there is challenging task of it that captured noisy images. Removing noise from images is difficult aspiration for image denoising technique. Therefore, various redundant blur and amounts of remaining noise ought to be analysis to research the particular results of denoising method. In this research article, different methods are used for image denoising and evaluated performance for wireless capsule endoscopy images. The proposed approach is suggested Bidimensional Empirical Mode Decomposition (BEMD) for WCE images. Here evaluate performance of BEMD method and wavelet. Computer simulation proved that proposed technique offer considerable advantage than other method.

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Enhanced Ulcer Recognition from Capsule Endoscopic Images Using Texture Analysis

Cited by 13 publications

References 58 publications

Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization

Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization

Deep Learning-Based Earlier Detection of Esophageal Cancer Using Improved Empirical Wavelet Transform From Endoscopic Image

A Novel Strategy to Image Decomposition for Wireless Capsule Endoscopy Images

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