Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item COVID-19 Waves and Their Impacts to Society(Institute of Electrical and Electronics Engineers Inc., 2023) Kumar, S.; Bhowmik, B.The COVID-19 pandemic has led to a global medical crisis and significant disruptions to daily life since its emergence in December 2019. Rapidly, it spread to 218 countries affecting more than 754 million people. The virus appears in different variants bringing significant implications at all societal levels. Recently, different variants of the virus have emerged, which have caused significant consequences in society. This paper presents the state-of-the-art on other waves caused by COVID-19 variants, their impacts on society, and challenges. The paper also details recent advancements to combat this disease. © 2023 IEEE.Item Verification of COVIFIND Test Kit for COVID-19(Institute of Electrical and Electronics Engineers Inc., 2024) Kumar, S.; Rathore, R.; Bhowmik, B.The ongoing global challenges of infectious diseases, particularly COVID-19, highlight the critical need for accurate and timely diagnostic tools. Rapid antigen test kits have become essential for swiftly detecting COVID-19 infections, enabling healthcare professionals to make prompt decisions based on quick results. Due to their complexity and the potential impact on public health, these kits require thorough validation. This paper presents a novel formal verification approach using predicate logic and state transition tables to validate the performance of COVIFIND COVID-19 antigen self-test kit. The proposed methodology encompasses sensitivity, specificity, and operational parameters, incorporating logical formulae and state transition rules to describe and verify test states and transitions. The frame-work has been applied to various test components, including sample pads, conjugate pads, and control lines, demonstrating its effectiveness in maintaining adherence to specified requirements and performance standards. The results affirm the robustness of the COVIFIND test kit in providing reliable results across diverse conditions. This work enhances the quality assurance processes for rapid diagnostic tests, lays the groundwork for further development, and is crucial for maintaining high standards in public health diagnostics. © 2024 IEEE.Item Detecting COVID-19 Infection Using Customized Convolutional Neural Network(Institute of Electrical and Electronics Engineers Inc., 2024) Kumar, S.; Kisku, B.; Vardhan K, S.H.; Kumar, S.; Bhowmik, B.The COVID-19 pandemic has affected 775 million people globally, with an estimated death toll of 7 million. Detection methods like reverse transcription polymer chain reaction (RT-PCR) face multiple challenges, including false positive cases, time-consuming, and high cost. A rapid, precise, affordable screening alternative is essential to expedite COVID-19 detection. Various efforts have focused on expediting COVID-19 detection due to the high costs and logistical challenges associated with traditional methods. This paper proposes a customized deep-learning framework architecture for automatically identifying COVID-19 infection in chest X-ray (CXR) images. Multiple neural networks extract deep features from the CXR images, including popular models such as VGG19, DenseNet201, EfficientNet, MobileNetV2, and InceptionV3. The proposed model undergoes training and testing using the QaTa-COVID-19 dataset. The proposed model achieves classification accuracy of 97.06%, with precision, recall, and F1 score rates for COVID-19 cases recorded at 97.34%, 96.36%, and 97.01%, respectively, for the 4-class cases (COVID vs. Normal vs. Pediatric Bacterial Pneumonia vs. Pediatric Viral Pneumonia). © 2024 IEEE.Item Automated Segmentation of COVID-19 Infected Lungs via Modified U-Net Model(Institute of Electrical and Electronics Engineers Inc., 2024) Kumar, S.; Bhowmik, B.The COVID-19 pandemic has led to significant outbreaks in more than 220 countries worldwide, profoundly impacting the public health and lives. As of February 2024, over 774 million cases have been reported, with more than 7,035,337 deaths recorded. Therefore, there is a significant need for automated image segmentation to serve as clinical decision support. This paper presents a novel automated segmentation framework that dynamically generates distinct and randomized image patches for training using preprocessing techniques and extensive data augmentation. The proposed architecture employs a semantic segmentation approach, ensuring accuracy despite limited data availability. Experimental assessment comprises a visual inspection of the predicted segmentation outcomes. Quantitative evaluation of segmentation includes standards performance metrics such as precision, recall, Dice score, and Intersection over Union (IoU). The results exhibit a remarkable Dice coefficient score of 98.3% and an IoU rate surpassing 96.8%, demonstrating the model's robustness in identifying COVID-19-infected lung regions. © 2024 IEEE.