Lymphoid hyperplasia of the colon and its association with underlying allergic airway diseases

Iwamuro, Masaya; Hiraoka, Sakiko; Okada, Hiroyuki; Kawai, Yosuke; Miyabe, Yoshio; Takata, Katsuyoshi; Yamamoto, Kazuhide

doi:10.1007/s00384-015-2392-6

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…A few studies have suggested that there is a relationship between asthma and lymphoid hyperplasia of nonrespiratory organs including the gastrointestinal tract. [18,19] For instance, as many as 85.7% of patients with lymphoid hyperplasia of the large intestine (cecum, colon, and rectum) have allergic airway diseases and positive reactions to inhalant allergens. [18,19] Because the appendix is an organ with a large number of lymphoid follicles, asthma-induced reactive lymphoid hyperplasia could manifest in the lumen of appendix to cause obstruction or inflammation in appendicitis.…”

Section: Discussionmentioning

confidence: 99%

Increased risk of appendectomy in patients with asthma

Kim

Park

et al. 2019

Medicine

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Section: Discussionmentioning

confidence: 99%

Increased risk of appendectomy in patients with asthma

Kim

Park

et al. 2019

Medicine

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“…LH occurs in the rapidly increasing lymphocytic cells. It has a spherical shape with color of light yellow or light gray [11]. LH can be related to several diseases such as immunodeficiency, HIV/AIDS, selective IgA deficiency, and Helicobacter pylori infections [12].…”

Section: Introductionmentioning

confidence: 99%

Combining deep features and hand-crafted features for abnormality detection in WCE images

Amiri

Hassanpour

Beghdadi³

2023

Multimed Tools Appl

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In this paper, a computer-aided method is proposed for abnormality detection Wireless Capsule Endoscopy (WCE) video frames. Common abnormalities in WCE images include ulcers, bleeding, Angiodysplasia, Lymphoid Hyperplasia, and polyp. In this paper, deep features and Hand-crafted features are combined to detect these abnormalities in WCE images. There are no sufficient images to train deep structures therefore the ResNet50 pertained model is used to extract deep features. Hand-crafted features are associated with color, shape, and texture. They are extracted from the region of interest (ROI), i.e. suspicious region. The expectation Maximization (EM) algorithm is used to extract more distinct areas in the background as ROI. The expectation Maximization (EM) algorithm is configured in a way that can extract the area with a distinct texture and color as ROI. The EM algorithm is also initialized with a new fast method which leads to an increase in the accuracy of the method. We also used a novel idea to reveal unexpected color changes in the background due to existing lesions as a feature set. A large number of features are created by the method, so the minimum redundancy maximum relevance approach is used to select a subset of more effective features. These selected features are then fed to a Support Vector Machine for classification. The results show that the proposed approach can detect mentioned abnormalities in WCE frames with the accuracy of 97.82%

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