A New Respiratory Diseases Detection Model in Chest X-Ray Images Using CNN
Traitement du Signal
; 40(1):145-155, 2023.
Article
in English
| Scopus | ID: covidwho-2291646
ABSTRACT
Convolutional Neural Network (CNN)-based deep learning techniques have recently demonstrated increased potential and effectiveness in image recognition applications, such as those involving medical images. Deep-learning models can recognize targets with performance comparable to radiologists when used with CXR. The primary goal of this research is to examine a deep learning technique used on the radiography dataset to detect COVID-19 in X-ray medical images. The proposed system consists of several stages, from pre-processing, passing through the feature reduction using more than one technique, to the classification stage based on a proposed model. The test was applied to the COVID-19 Radiography dataset of normal and three lung infections (COVID-19, Viral Pneumonia, and Lung Opacity). The proposed CNN model has shown its ability to classify COVID, normal, and other lung infections with perfect accuracy of 99.94%. Consequently, the AI-based early-stage detection algorithms will be enhanced, increasing the accuracy of the X-raybased modality for the screening of various lung diseases. © 2023 Lavoisier. All rights reserved.
convolutional neural network (CNN); COVID-19; gray level co-occurrence matrix (GLCM); linear discriminant analysis (LDA); radiography; Biological organs; Convolution; Convolutional neural networks; Deep learning; Diagnosis; Discriminant analysis; Image recognition; Learning algorithms; Learning systems; Medical imaging; Statistical tests; Convolutional neural network; Disease detection; Gray level co-occurrence matrix; Gray-level co-occurrence matrix; Grey-level co-occurrence matrixes; Learning techniques; Linear discriminant analyse; Linear discriminant analyze; Lung infection
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Traitement du Signal
Year:
2023
Document Type:
Article
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