A Novel Fusion Model of Hand-Crafted Features With Deep Convolutional Neural Networks for Classification of Several Chest Diseases Using X-Ray Images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10252812" target="_blank" >RIV/61989100:27240/23:10252812 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10103528" target="_blank" >https://ieeexplore.ieee.org/document/10103528</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3267492" target="_blank" >10.1109/ACCESS.2023.3267492</a>

Result language

angličtina

Original language name

A Novel Fusion Model of Hand-Crafted Features With Deep Convolutional Neural Networks for Classification of Several Chest Diseases Using X-Ray Images

Original language description

The results of recent research suggest that radiological images include important information related several chest diseases. As a result, the use of deep learning to assist in the automated diagnosis of chest diseases may prove useful as a diagnostic tool in the future. In this study, we propose a novel fusion model of hand-crafted features with deep convolutional neural networks (DCNNs) for classifying ten different chest diseases using chest X-rays. The method that has been suggested is split down into three distinct parts. The first step involves utilizing the Info-MGAN network to perform segmentation on the raw CXR data to construct lung images. In the second step, the segmented lung images are fed into a novel pipeline that extracts discriminatory features by using hand-crafted techniques such as SURF and ORB, and then these extracted features are fused to the trained DCNNs. At last, various machine learning models have been used as the last layer of the DCNN models for the classification of chest diseases. Comparison is made between the performance of various proposed architectures for classification, all of which integrate DCNNs, key point extraction methods, and machine learning models. The robustness of the model was further confirmed by statistical analyses of the datasets using McNemar&apos;s and ANOVA tests respectively.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Volume of the periodical

11

Issue of the periodical within the volume

11

Country of publishing house

US - UNITED STATES

Number of pages

26

Pages from-to

39243-39268

UT code for WoS article

000979908800001

EID of the result in the Scopus database

2-s2.0-85153493471