Experimental study on generalization capability of extended naive Bayesian classifier

Chen, Sisi; Cao, Juanjuan; Li-li, Gan; Song, Qing; Han, Di

doi:10.1007/s13042-014-0311-8

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…We used PNN, 44 (kNN), 45 (LDA), 46 and (SVM) classifiers with their various kernels such as (RBF), polynomial kernels of order 1, 2, and 3, 47 (DT), 45 (QDA), 48 and NB categorizer 49 . The performance evaluation was carried out in terms of accuracy, sensitivity, specificity, and PFV 50 …”

Section: Introductionmentioning

confidence: 99%

Implementation of convolutional neural network categorizers in coronary ischemia detection

Wei

Gao

Kumar

et al. 2020

Int J Imaging Syst Tech

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The heart is one of the most important and sophisticated organ of the human body. Coronary ischemia is a condition in which the coronary muscles do not receive sufficient blood and oxygen because of blocked or tightened heart vessels. This syndrome is called cardiac vessel illness. There have been numerous attempts to detect the impact of cardiac vessel illness on the heart muscles using noninvasive experiments. Most of the effects of ischemia as well as severe cardiac conditions on the muscles of the ventricle parts can be detected using ultrasonic images. If treatment is provided to suspected cases in the early stage of cardiac vessel illness, the chance of survival is high; for this, many software‐based detection approaches have been used. In this study, we propose an approach that can automatically diagnose the cardiac artery disease by using the cardiac echo images of the four parts of the heart.

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