The effect of image feature qualifiers on fuzzy colorectal polyp detection schemes using KH interpolation - towards hierarchical fuzzy classification of coloscopic still images

2018 ◽  
Author(s):  
Szilvia Nagy ◽  
Brigita Sziova ◽  
Laszlo T. Koczy
Keyword(s):  
Colorectal Polyp ◽  
Fuzzy Classification ◽  
Image Feature ◽  
Polyp Detection ◽  
Still Images ◽  
Detection Schemes

scholarly journals Computed Tomography Colonography as a Sensible Option for Colorectal Cancer Screening: Evidence Based on Metanalysis

2021 ◽  
Vol 41 (01) ◽  
pp. 087-095
Author(s):  
Ingrid Chaves de Souza Borges ◽  
Natália Costa Resende Cunha ◽  
Amanda Marsiaj Rassi ◽  
Marcela Garcia de Oliveira ◽  
Jacqueline Andréia Bernardes Leão-Cordeiro ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Computed Tomography ◽  
Cancer Screening ◽  
Literature Search ◽  
Web Of Science ◽  
Screening Method ◽  
Colorectal Polyp ◽  
Evidence Based ◽  
Polyp Detection ◽  
Computed Tomography Colonography

Abstract Objective This metanalysis aimed to evaluate the sensitivity and specificity of computed tomography colonography in colorectal polyp detection. Methods A literature search was performed in the PubMed and Web of Science databases. Results A total of 1,872 patients (males 57.2%, females 42.8%) aged 49 to 82 years old (mean age 59.7 ± 5.3 years) were included in this metanalysis. The estimated sensitivity of computed tomography colonography was 88.4% (46.3–95.7%, coefficient of variation [CV] = 28.5%) and the estimated specificity was 73.6% (47.4–100.0%, CV = 37.5%). For lesions up to 9 mm, the sensitivity was 82.5% (62.0–99.9%, CV = 25.1%) and the specificity was 79.2% (32.0–98.0%, CV = 22.9%). For lesions > 9 mm, the sensitivity was 90.2% (64.0–100.0%, CV = 7.4%) and the specificity was 94.7% (80.0–100.0%, CV = 6.2%). No statistically significant differences in sensitivity according to the size of the lesion were found (p = 0.0958); however, the specificity was higher for lesions > 9 mm (p < 0.0001). Conclusions Most of the studies analyzed in the present work were conducted before 2010, which is about a decade after computed tomography colonography started being indicated as a screening method by European and American guidelines. Therefore, more studies aimed at analyzing the technique after further technological advancements are necessary, which could lead to the development of more modern devices.

scholarly journals Use of KNN Classifier for Emotion Recognition Based on Distance Measures

2019 ◽  
Vol 9 (1) ◽  
pp. 1294-1298
Keyword(s):  
Gabor Filter ◽  
Texture Features ◽  
Distance Measures ◽  
Still Images ◽  
City Block ◽  
Lighting Conditions ◽  
Knn Classifier ◽  
The Face ◽  
City Block Distance

Human-computer interaction (HCI), in recent times, is gaining a lot of significance. The systems based on HCI have been designed for recognizing different facial expressions. The application areas for face recognition include robotics, safety, and surveillance system. The emotions so captured aid in predicting future actions in addition to providing valuable information. Fear, neutral, sad, surprise, happy are the categories of primary emotions. From the database of still images, certain features can be obtained using Gabor Filter (GF) and Histogram of Oriented Gradient (HOG). These two techniques are being used while extracting features for getting the expressions from the face. This paper focuses on the customized classification of GF and HOG using the KNN classifier.GF provides texture features whereas HOG finds applications for images exhibiting differing lighting conditions. Simplicity and linearity of KNN classifier appeals for its use in the present application. The paper also elaborates various distances used in KNN classifiers like city-block, Euclidean and correlation distance. This paper uses Matlab implementation of GF, HOG and KNN for extracting the required features and classification, respectively. Results exhibit that the accuracy of city- block distance is more .

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