ABSTRACT
Background: Progress in reducing malaria incidence and deaths has stalled, in part due to limited access to quality malaria testing and treatment amongst rural populations. This time-series analysis aims to describe changes in rural malaria diagnosis and treatment before and during the rollout of Liberia's National Community Health Assistant (CHA) program. It also explores how malaria service delivery changed during the COVID-19 epidemic.
ABSTRACT
Automatic disease detection using machine learning-based techniques from X-ray and computed tomography (CT) can play a major role in the frontline to assist medical professionals during the current outbreak of COVID-19. Fast diagnosis of the disease is the key to reduce the uncontrollable spread of this life-threatening disease, where machine learning-based applications can contribute greatly by predicting the situation of patients so that professionals can decide accordingly. The major drawbacks of detecting COVID-19 are its similarities with different types of pneumonia, and the absence of properly labeled data. Considering the ResNet152V2 as a backbone network, an efficient architecture, namely ResCovNet is proposed to detect COVID-19 accurately from chest CT scan images by separating it from three types of pneumonia and normal cases. Otsu's thresholding is applied in the pre-processing step to strengthen the features for the classification network. With the use of proposed architecture, a very satisfactory classification accuracy of 88.1% is achieved to separate COVID-19 from all other four classes. Evaluating the performance of this study by 3-fold cross-validation, and comparison with related works prove that this adroit algorithm provides an effective way to be implemented as a diagnostic tool in the COVID-19 screening. © 2020 IEEE.