Browsing by Author "Vats, Satvik"

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Citation Count: 2
Incremental learning-based cascaded model for detection and localization of tuberculosis from chest x-ray images
(Pergamon-elsevier Science Ltd, 2024) Vats, Satvik; Salahshour, Soheıl; Singh, Karan; Katti, Anvesha; Ariffin, Mazeyanti Mohd; Ahmad, Mohammad Nazir; Salahshour, Soheil
Rapid treatment protocols such as X-ray and CT scans have played a crucial role in the diagnosis of tuberculosis (TB infection). Automatic detection of CXR is required to speed up patient treatment with accuracy. Consequently, it reduces the burden of patients on medical practitioners. The present paper proposes an incremental learning-based cascaded (ILCM) model to detect tuberculosis from Chest X-ray images. The proposed model also localizes the infected region on the CXR image. The experimental outcome, clearly indicates that the performance is better than the pre-trained model as tested on the local population data (93.20% overall accuracy), F1 score of 97.23% (harmonic mean of precision and recall). Where the Golden standard dataset was 83.32% overall accuracy, and F1 score 82.24%.
Citation Count: 0
Iterative enhancement fusion-based cascaded model for detection and localization of multiple disease from CXR-Images
(Pergamon-elsevier Science Ltd, 2024) Vats, Satvik; Sharma, Vikrant; Singh, Karan; Singh, Devesh Pratap; Bajuri, Mohd Yazid; Taniar, David; Ahmadian, Ali
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.