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001-es BibID:	BIBFORM123055
Első szerző:	Rana, Jassim Mohammed
Cím:	A Robust Hybrid Machine and Deep Learning-based Model for Classification And Identification in Chest X-ray Images / Rana Jassim Mohammed, Mudhafar Jalil Jassim Ghrabat, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Iman Qays Abduljaleel, Ali Hasan Ali, Dhafer G. Honi, Husam A. Neamah
Dátum:	2024
ISSN:	2241-4487 1792-8036
Megjegyzések:	Successful medical treatment for patients with COVID-19 requires rapid and accurate diagnosis. Fighting the COVID-19 pandemic requires an automated system to diagnose the virus on Chest X-Ray (CXR) images. CXR images are frequently used in healthcare as they offer the potential for rapid and accurate disease diagnosis. SARS-CoV-2 targets the respiratory system, resulting in pneumonia with additional symptoms, such as dry cough, fatigue, and fever, which could be misdiagnosed as pneumonia, TB, or lung cancer. There is difficulty in differentiating the features of COVID-19 from other diseases that have similarities in CXR images. Automated Computer-Aided Diagnosis (CAD) systems incorporate machine or deep learning methods to improve efficiency and accuracy. CNNs are among the most widely used methods, as they have shown encouraging accuracy in identifying COVID-19 in CXR images. This study presents a hybrid deep learning model to provide faster diagnosis of COVID-19 infection using CXR images. The Densenet201 model was used for feature extraction and a Multi-Layer Perceptron (MLP) was used for classification. The proposed method achieved 98.82% accuracy and similar sensitivity, specificity, precision, recall, and F1 score. These results are promising when compared to other DL models trained in similar datasets.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk COVID-19 Chest X-Ray (CXR) DL ML densenet201 MLP
Megjelenés:	Engineering, Technology and Applied Science Research. - 14 : 5 (2024), p. 16212-16220. -
További szerzők:	Mudhafar, Jalil Jassim Ghrabat Zaid, Ameen Abduljabbar Vincent, Omollo Nyangaresi Iman, Qays Abduljaleel Ali, Ali Hasan (1989-) (matematikus) Alshuwaili, Dhafer Gheni Honi (1991) (Informatics)(PhD) Neamah, Husam A. (1990-) (mérnök)
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat
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