Browsing by Author "Singh, Karan"

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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.
Citation Count: 1
MADM-based network selection and handover management in heterogeneous network: A comprehensive comparative analysis
(Elsevier, 2024) Yadav, Ashok Kumar; Singh, Karan; Arshad, Noreen Izza; Ferrara, Massimiliano; Ahmadian, Ali; Mesalam, Yehya I.
As radio access technologies, processing speeds, and multimode interfaces of low -powered portable devices continue to advance, the future of wireless communication is envisioned to offer pervasive network coverage, high data rates, and a wide spectrum of services while maintaining high mobility. High data rates, wide range of services, huge connectivity, capacity, and good geographic coverage are being provided by the ultra -dense deployment of small base stations (BSs) in heterogeneous wireless networks (HWN). But dense deployment of small BSs, high mobility, network heterogeneity, imbalanced traffic, and dynamic user preferences lead to frequent handover. Network overhead, excessive energy consumption, and a decrease in service quality and user satisfaction can be due to frequent handover. So, handover management is one of the crucial challenges in the implementation of 5G and beyond in HWNs for ensuring seamless connectivity, energy efficiency, and the required quality of services and experiences. The effectiveness of handover decisions in HWNs relies on the implementation of a suitable network selection mechanism. Multi -attribute decision -making (MADM) is being used to model and analyze appropriate network selection complexities by considering a broad spectrum of intricate and conflicting decision criteria for efficient handover decisions in HWN. This article extensively explores, compares, and analyzes vital MADM techniques utilized for modeling appropriate network selection strategies in terms of algorithmic strategies, cardinality, types and significance of decision attributes, and network utilities. This article also examines, analyzes, and recognizes the recent mobility management challenges and trends in utilizing MADM strategies to tackle network selection issues in high-speed HWNs.