Iterative enhancement fusion-based cascaded model for detection and localization of multiple disease from CXR-Images

The lungs are a vital organ of the human body. Malfunctioning of the lungs caused a direct threat to life. In recent years the world has witnessed massive medical insufficiency to handle the lung diseases caused by numerous agents including COVID-19. According to the recommended course of treatment,...

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Published inExpert systems with applications Vol. 255; p. 124464
Main Authors Vats, Satvik, Sharma, Vikrant, Singh, Karan, Pratap Singh, Devesh, Yazid Bajuri, Mohd, Taniar, David, Innab, Nisreen, Mouldi, Abir, Ahmadian, Ali
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.12.2024
Subjects
Artificial intelligence
Cascaded Model
Chest X-Ray
COVID-19
Enhancement Fusion
FRCNN
Multi Disease
Radiology
COVID-19
Chest X-Ray
Enhancement Fusion
FRCNN
Radiology
Cascaded Model
Artificial intelligence
Multi Disease
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Summary:The lungs are a vital organ of the human body. Malfunctioning of the lungs caused a direct threat to life. In recent years the world has witnessed massive medical insufficiency to handle the lung diseases caused by numerous agents including COVID-19. According to the recommended course of treatment, medical imaging tests including X-rays and CT scans have been very helpful in identifying multiple chest infections. Automatic detection of chest disease is the need of the modern time as it will speed up patient care and reduce doctors’ workload. An Iterative Enhancement Fusion-based Cascaded (IEFCM) model to identify multiple diseases from chest X-ray images is suggested in the present paper. If a chest infection is discovered in the imaging, the suggested model additionally localizes the precise infected area on the CXR image. Experimental outcome clearly demonstrates that the performance of suggested model is significantly superior to the pre-trained model, that is the Golden standard dataset and data from the local population. In terms of sensitivity and specificity, IEFCM achieved 95.62 % sensitivity, which indicates an accurate diagnosis of lung disease, reducing the risk of missing any instances. Similarly, the specificity is 96.23 %, which denotes, the IEFCM model correctly identified the healthy people. It resulted decrease of misdiagnosis and unnecessary follow-up testings.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2024.124464