Ensembling of Efficient Deep Convolutional Networks and Machine Learning Algorithms for Resource Effective Detection of Tuberculosis using Thoracic (Chest) Radiography (June 2022)

Tuberculosis (TB) is a communicable pulmonary disorder and countries with low and middle-income share a higher TB burden as compared to others. The year 2020-2021 universally saw a brutal pandemic in the form of COVID-19, that crushed various lives, health infrastructures, programs, and economies worldwide at an unprecedented speed. The gravity of this estimation gets intensified in systems with limited technological advancements. To assist in the identification of tuberculosis, we propose the ensembling of efficient deep convolutional networks and machine learning algorithms that do not entail heavy computational resources. In this paper, the three of the most efficient deep convolutional networks and machine learning algorithms are employed for resource-effective (low computational and basic Imaging requirements) detection of Tuberculosis. The pivotal features extracted from the deep networks are ensembled and subsequently, the machine learning algorithms are used to identify the images based on the extracted features. The said model underwent k-fold cross-validation and achieved an accuracy of 87.90% and 99.10% with an AUC of 0.94 and 1 respectively in identifying TB infected images from Normal and COVID infected images. Also, the model’s error rate, F-score, and youden’s index values of 0.0093, 0.9901, and 0.9812 for TB versus COVID identification along with the model’s accuracy claim that its use can be beneficial in identifying TB infections amid this COVID-19 pandemic, predominantly in countries with limited resources. Author

A Decision-Level Fusion Method for COVID-19 Patient Health Prediction

With the continuous attempts to develop effective machine learning methods, information fusion approaches play an important role in integrating data from multiple sources and improving these methods' performance. Among the different fusion techniques, decision-level fusion has unique advantages to fuse the decisions of various classifiers and getting an effective outcome. In this paper, we propose a decision-level fusion method that combines three well-calibrated ensemble classifiers, namely, a random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGB) methods. It is used to predict the COVID-19 patient health for early monitoring and efficient treatment. A soft voting technique is used to generate the final decision result from the predictions of these calibrated classifiers. The method uses the COVID-19 patient's health information, travel demographic, and geographical data to predict the possible outcome of the COVID-19 case, recovered, or death. A different set of experiments is conducted on a public novel Corona Virus 2019 dataset using a different ratio of test sets. The experimental results show that the proposed fusion method achieved an accuracy of 97.24% and an F1-score of 0.97, which is higher than the current related work that has an accuracy of 94% and an F1-score 0.86, on 20% test set taken from the dataset. (C) 2021 Elsevier Inc. All rights reserved.

Privacy Preservation of User Identity in Contact Tracing for COVID-19-like Pandemics Using Edge Computing

Pandemic and infectious disease outbreaks put pressure on health authorities and require lockdowns. These outbreaks, which strain limited healthcare resources, must be swiftly controlled and monitored. A large number of healthcare authorities are currently investigating automated systems to support outbreak monitoring and control. However, current contact tracing systems face many privacy, participation, and power constraints. Furthermore, elderly or less financially able individuals often cannot participate in automated contact tracing due to not owning a smartphone. This paper proposes a new system that enables health authorities to track exposure among individuals participating in the automated system, aid health authorities in interviewing non-participating individuals, and minimize the processing required by offloading to nearby edge computing devices. The proposed system utilizes edge servers to assist health authorities in tracking users who withdraw from or are unable to use contact tracing. Edge computing devices have access to more contextual information, resulting in minimal data collection and thus enabling businesses, houses, and offices to participate in contact tracing as locations. Edge computing devices enable location-based data collection of contact tracing data using proximity-based sensors for offices, homes, and shops, thereby assisting health authorities to notify users of exposure without disclosing the identities of businesses or individuals. Moreover, the proposed system reduces the overall power for end users up to 97% by delegating contact tracing to nearby edge computing devices. In addition, the system mitigates data poisoning attacks that target individualsx2019;smartphones, edge devices, or cloud servers by utilizing blockchain to store contacts, delegations, and identities. Author