Stomach Deformities Recognition Using Rank-Based Deep Features Selection

Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient’s deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complet...

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Vydané v:	Journal of medical systems Ročník 43; číslo 12; s. 329
Hlavní autori:	Khan, Muhammad Attique, Sharif, Muhammad, Akram, Tallha, Yasmin, Mussarat, Nayak, Ramesh Sunder
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.12.2019 Springer Nature B.V
Predmet:	Abnormalities Artificial neural networks Capsule Endoscopy - methods Classification Colorectal cancer Computed tomography Computer simulation Endoscopy Entropy Feature extraction Feature recognition Health Informatics Health Informatics and Computer Vision Health Sciences Hemorrhage - diagnosis Hemorrhage - diagnostic imaging Humans Image & Signal Processing Image classification Image processing Image Processing, Computer-Assisted - methods Image segmentation Magnetic resonance imaging Medicine Medicine & Public Health Neural networks Neural Networks, Computer Optimization Physicians Recent Advances in Deep Learning for Biomedical Signal Processing Statistics for Life Sciences Stomach Diseases - diagnosis Stomach Diseases - diagnostic imaging Stomach Ulcer - diagnostic imaging Transfer learning WCE Deep features selection Saliency estimation Features fusion Colorectal cancer
ISSN:	0148-5598, 1573-689X, 1573-689X
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Abstract	Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient’s deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur’s entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15 s.
AbstractList	Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient’s deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur’s entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15 s. Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient’s deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur’s entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15 s. Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient's deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur's entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15 s.Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient's deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur's entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15 s.
ArticleNumber	329
Author	Akram, Tallha Yasmin, Mussarat Khan, Muhammad Attique Nayak, Ramesh Sunder Sharif, Muhammad
Author_xml	– sequence: 1 givenname: Muhammad Attique surname: Khan fullname: Khan, Muhammad Attique organization: Department of CS&E, HITEC University – sequence: 2 givenname: Muhammad surname: Sharif fullname: Sharif, Muhammad email: muhammadsharifmalik@yahoo.com organization: Department of E&CE, COMSATS University Islamabad – sequence: 3 givenname: Tallha surname: Akram fullname: Akram, Tallha organization: Information Science, Canara Engineering College – sequence: 4 givenname: Mussarat surname: Yasmin fullname: Yasmin, Mussarat organization: Department of E&CE, COMSATS University Islamabad – sequence: 5 givenname: Ramesh Sunder surname: Nayak fullname: Nayak, Ramesh Sunder organization: Department of CS, COMSATS University Islamabad
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Copyright	Springer Science+Business Media, LLC, part of Springer Nature 2019 Journal of Medical Systems is a copyright of Springer, (2019). All Rights Reserved.
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Snippet	Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient’s deformity during the review time. Among set of... Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient's deformity during the review time. Among set of...
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SubjectTerms	Abnormalities Artificial neural networks Capsule Endoscopy - methods Classification Colorectal cancer Computed tomography Computer simulation Endoscopy Entropy Feature extraction Feature recognition Health Informatics Health Informatics and Computer Vision Health Sciences Hemorrhage - diagnosis Hemorrhage - diagnostic imaging Humans Image & Signal Processing Image classification Image processing Image Processing, Computer-Assisted - methods Image segmentation Magnetic resonance imaging Medicine Medicine & Public Health Neural networks Neural Networks, Computer Optimization Physicians Recent Advances in Deep Learning for Biomedical Signal Processing Statistics for Life Sciences Stomach Diseases - diagnosis Stomach Diseases - diagnostic imaging Stomach Ulcer - diagnostic imaging Transfer learning
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Title	Stomach Deformities Recognition Using Rank-Based Deep Features Selection
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