ChestX-Ray6 Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network

ChestX-Ray6 : Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network

© 2022 Elsevier Ltd. All rights reserved.

Bibliographische Detailangaben
Veröffentlicht in:Expert systems with applications. - 1999. - 211(2023) vom: 01. Jan., Seite 118576
1. Verfasser: Nahiduzzaman, Md (VerfasserIn)
Weitere Verfasser: Islam, Md Rabiul, Hassan, Rakibul
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2023
Zugriff auf das übergeordnete Werk:Expert systems with applications
Schlagworte:Journal Article COVID19 Cardiomegaly ChestX-Ray6 Convolutional neural network (CNN) DenseNet121 Lung opacity MobileNetV2 Pleural Pneumonia mehr... ResNet50 VGG19
Beschreibung
Zusammenfassung:© 2022 Elsevier Ltd. All rights reserved.
In the last few decades, several epidemic diseases have been introduced. In some cases, doctors and medical physicians are facing difficulties in identifying these diseases correctly. A machine can perform some of these identification tasks more accurately than a human if it is trained correctly. With time, the number of medical data is increasing. A machine can analyze this medical data and extract knowledge from this data, which can help doctors and medical physicians. This study proposed a lightweight convolutional neural network (CNN) named ChestX-ray6 that automatically detects pneumonia, COVID19, cardiomegaly, lung opacity, and pleural from digital chest x-ray images. Here multiple databases have been combined, containing 9,514 chest x-ray images of normal and other five diseases. The lightweight ChestX-ray6 model achieved an accuracy of 80% for the detection of six diseases. The ChestX-ray6 model has been saved and used for binary classification of normal and pneumonia patients to reveal the model's generalization power. The pre-trained ChestX-ray6 model has achieved an accuracy and recall of 97.94% and 98% for binary classification, which outweighs the state-of-the-art (SOTA) models
Beschreibung:Date Revised 09.09.2024
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:0957-4174
DOI:10.1016/j.eswa.2022.118576