Machine learning-aided classification of Covid-19 in lung Ultrasound Images.
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With the rapid development of COVID-19 into a global pandemic, Uganda is in dire need of screening tools to aid risk strati cation for con rmatory testing, quarantining as well as facilitate appropriate allocation of the limited health resources. Lately ultrasound has shown relevance and dominance in respiratory screening due to its far-reaching availability, ease of use and non-radiation competence. Lung ultrasonography (LUS) requires vivid knowledge of identi cation of features i.e. A-lines which are a visual pattern which essentially represent a healthy lung, B-lines, pleural e usion and lung consolidation which are prominent COVID-19 indicative artefacts. However, the accuracy and ease with which these artefacts can be spotted is highly dependent on the expertise and experience of the sonographer. Therefore, there's need for a uni ed pathway for the interpretation and classi cation of COVID-19 in LUS images. In literature, deep learning has been shown to meet or exceed clinician performance across most visual elds of medicine thus past work has been done in Localizing B-lines in Lung Ultrasonography by Weakly-Supervised Deep Learning. This project involved detecting and localizing B lines by employing modern deep learning strategies to support clinicians in clinical assessments. However, this still involved interpretation which is subject to cognitive bias and visual limitations. Thus, the proposed solution, machine learning- aided screening of COVID-19 in LUS images which alleviates the observer variability among radiologists by automation of classi cation of LUS images. The machine learning-aided screening of COVID-19 LUS images project involves automatic artefacts detection and interpretation which not only exceeds the limits of human vision and cognitive bias but also robustly and accurately avails results on a web-based application platform. This project has three contributions. First, we gathered a lung ultrasound dataset consisting of 792 images (451 COVID-19 images and 341 HEALTHY images). This dataset was assembled from various online sources, pre-processed speci cally for deep learning models and split in a strati ed manner to obtain the train, validation and test set. Second, three machine learning models i.e., VGG 16, VGG 19 and Resnet were successfully built, trained and tested using the same binary class dataset. VGG 16 achieved an outstanding performance with a classi fication accuracy of 98%, recall of 1, precision of 96%, F1 score of 97.82% and ROC AUC of 99.9%. Lastly, the outperforming VGG 16 model was deployed on a decision support web-based application. 1