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2022 IEEE Region 10 Symposium, TENSYMP 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2052085


The healthcare sector plays a significant role in the industry, where a client looks for the highest amount of care and services, no matter the cost. However, this sector has not satisfied society's presumption, even if this industry consumes a considerable percentage of the national budget. In the past, medical experts have been looking for smart medical solutions. This work focuses on accurate and early detection of illness from various medical images. Early detection not only aids in the development of better medications but can also save a life in the long run. Deep learning provides an excellent solution for early medical imaging in healthcare. This paper proposed a Stacked-based BiLSTM with Resnet50 Model using an AdaSwarm optimizer to classify and analyze the medical illnesses from the different medical image datasets. For this study, four medical datasets were used as benchmarks: Covid19, Pneumonia, Ma, and Lung Cancer. Accuracy, AUC, ROC, and F1 Score performance metrics are used to evaluate the prosed model from other models. The proposed model gives a mean ACCURACY, AUC, ROC, and F1 Score on these four datasets are 98%, 99%, 97%, and 98%, respectively. © 2022 IEEE.

6th International Conference on ICT for Sustainable Development, ICT4SD 2021 ; 314:905-913, 2022.
Article in English | Scopus | ID: covidwho-1653381


Over the last decade, there has been a quantum leap in terms of the evolution of new methodologies to better our quest to understand artificial intelligence and machine learning. One such field, where there has been an unparalleled advancement, is computer vision. The paper aims to design and structure an automated monitoring system that automates the monitoring of the number of people in this COVID-19 scenario in a designated enclosure. We have deployed the system on Raspberry Pi module and integrated a HOG detector which transcends ordinary Haar cascades in terms of performance. This model can then subsequently be connected and integrated with other modules to further enhance its applicability and spectrum of usage. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.