Effect of COVID-19 Infodemic on Media Trust and Perceived Stress

Background: Health infodemic undermines public health response, results in poor observance of public health measures and costs lives. Health campaigns will not produce intended results without controlling misinformation. This study aimed to analyzed the correlation between infodemic, COVID-19 stress and media trust. Subjects and Method: This was a cross sectional study conducted using online structured questionnaire, from December 2020 to January 2021. A total of 470 participants among African twitter community were randomly selected for this study. The dependent variables were COVID-19 stress and media trust. The independent variable was while Infodemic serve. The data was analysed using Pearson’s product moment correlation coefficient test. Results: COVID-19 stress  (r= 0.369; p<0.001) and media trust (r= 0.301; p<0.001) were correlated with infodemic and it was statistically significant. Conclusion: infodemic is correlated with COVID-19 stress and media trust. Keywords: infodemic, health communication, media trust, stress, COVID-19 Correspondence: Sanni Shamsudeen Ademola. Department of Computer Science, Faculty of Science and Engineer­ing, University of Eswatini, Private Bag 4, Matsapha, manzana, Kingdom of Eswatini. Email: [email protected]. Mobile: +26876241155/79241155. Journal of Health Promotion and Behavior (2021), 06(02): 144-153 DOI: https://doi.org/10.26911/thejhpb.2021.06.02.0

Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models

One of the most promising research initiatives in the healthcare field is focused on the rising incidence of skin cancer worldwide and improving early discovery methods for the disease. The most significant factor in the fatalities caused by skin cancer is the late identification of the disease. The likelihood of human survival may be significantly improved by performing an early diagnosis followed by appropriate therapy. It is not a simple process to extract the elements from the photographs of the tumors that may be used for the prospective identification of skin cancer. Several deep learning models are widely used to extract efficient features for a skin cancer diagnosis; nevertheless, the literature demonstrates that there is still room for additional improvements in various performance metrics. This study proposes a hybrid deep convolutional neural network architecture for identifying skin cancer by adding two main heuristics. These include Xception and MobileNetV2 models. Data augmentation was introduced to balance the dataset, and the transfer learning technique was utilized to resolve the challenges of the absence of labeled datasets. It has been detected that the suggested method of employing Xception in conjunction with MobileNetV2 attains the most excellent performance, particularly concerning the dataset that was evaluated: specifically, it produced 97.56% accuracy, 97.00% area under the curve, 100% sensitivity, 93.33% precision, 96.55% F1 score, and 0.0370 false favorable rates. This research has implications for clinical practice and public health, offering a valuable tool for dermatologists and healthcare professionals in their fight against skin cancer