Social media (and the world at large) have been awash with news of the COVID-19 pandemic. With the passage of time, news and awareness about COVID-19 spread like the pandemic itself, with an explosion of messages, updates, videos, and posts. Mass hysteria manifest as another concern in addition to the health risk that COVID-19 presented. Predictably, public panic soon followed, mostly due to misconceptions, a lack of information, or sometimes outright misinformation about COVID-19 and its impacts. It is thus timely and important to conduct an ex post facto assessment of the early information flows during the pandemic on social media, as well as a case study of evolving public opinion on social media which is of general interest. This study aims to inform policy that can be applied to social media platforms; for example, determining what degree of moderation is necessary to curtail misinformation on social media. This study also analyzes views concerning COVID-19 by focusing on people who interact and share social media on Twitter. As a platform for our experiments, we present a new large-scale sentiment data set COVIDSENTI, which consists of 90 000 COVID-19-related tweets collected in the early stages of the pandemic, from February to March 2020. The tweets have been labeled into positive, negative, and neutral sentiment classes. We analyzed the collected tweets for sentiment classification using different sets of features and classifiers. Negative opinion played an important role in conditioning public sentiment, for instance, we observed that people favored lockdown earlier in the pandemic; however, as expected, sentiment shifted by mid-March. Our study supports the view that there is a need to develop a proactive and agile public health presence to combat the spread of negative sentiment on social media following a pandemic.

Breast cancer (BC) is the second most prevalent type of cancer among women leading to death, and its rate of mortality is very high. Its effects will be reduced if diagnosed early. BC’s early detection will greatly boost the prognosis and likelihood of recovery, as it may encourage prompt surgical care for patients. It is therefore vital to have a system enabling the healthcare industry to detect breast cancer quickly and accurately. Machine learning (ML) is widely used in breast cancer (BC) pattern classification due to its advantages in modelling a critical feature detection from complex BC datasets. In this paper, we propose a system for automatic detection of BC diagnosis and prognosis using ensemble of classifiers. First, we review various machine learning (ML) algorithms and ensemble of different ML algorithms. We present an overview of ML algorithms including ANN, and ensemble of different classifiers for automatic BC diagnosis and prognosis detection. We also present and compare various ensemble models and other variants of tested ML based models with and without up-sampling technique on two benchmark datasets. We also studied the effects of using balanced class weight on prognosis dataset and compared its performance with others. The results showed that the ensemble method outperformed other state-of-the-art methods and achieved 98.83% accuracy. Because of high performance, the proposed system is of great importance to the medical industry and relevant research community. The comparison shows that the proposed method outperformed other state-of-the-art methods.

Pathology visual question answering (PathVQA) attempts to correctly answer medical questions presented with pathology images. Despite its great prospective in healthcare, the technology is still in its early stages with low overall accuracy. This is because it requires both high and low-level interactions on both the image (vision) and question (language) to generate an answer. Existing methods focused on treating vision and language features independently, which cannot capture these high and low-level interactions. Further, these methods failed to interpret retrieved answers, which are obscure to humans. Models interpretability to justify the retrieved answers has remained largely unexplored and has become important to engender users trust in the retrieved answer by providing insight into the model prediction. Motivated by these gaps, we introduce an interpretable transformer-based Path-VQA (TraP-VQA), where we embed transformers' encoder layers with vision (images) features extracted using CNN and language (questions) features extracted using CNNs and domain-specific language model (LM). A decoder layer of the transformer is then embedded to upsample the encoded features for the final prediction for PathVQA. Our experiments showed that our TraP-VQA outperformed state-of-the-art comparative methods with the public PathVQA dataset. Further, our ablation study presents the capability of each component of our transformer-based vision-language model. Finally, we demonstrate the interpretability of Trap-VQA by presenting the visualization results of both text and images used to explain the reason for a retrieved answer in the PathVQA.