Biomedical and Health Sciences

TB (Tuberculosis) is a contagious disease which is caused by a bacterium named Mycobacterium Tuberculosis. Screening is done to confirm the presence of TB using different screening techniques available i.e. Chest X-ray, Microscopy, Gene Xpert and Culture etc. Medical image processing is a rapidly growing field of image processing that is used to automate different medical procedures. In this research we have designed two automated systems for the screening of TB patients. A sample of 50 images for microscopy slides and chest X-ray radiographs were taken.

This cell images dataset is collected using an ultrafast imaging system known as asymmetric-detection time-stretch optical microscopy (ATOM)  for training and evaluation. This novel imaging approach can achieve label-free and high-contrast flow imaging with good cellular resolution images at a very high speed. Each acquired image belongs to one of the four classes: THP1, MCF7, MB231 and PBMC.

Recognition of human activities is one of the most promising research areas in artificial intelligence. This has come along with the technological advancement in sensing technologies as well as the high demand for applications that are mobile, context-aware, and real-time. We have used a smart watch (Apple iWatch) to collect sensory data for 14 ADL activities (Activities of Daily Living). 

Electronic Health Records and clinical longitudinal data have been visualized in a wide range of applications to assist the understanding of the status and evolution of patients. Few studies have objectively assessed these applications. We utilized the insights-based method to objectively assess the effectiveness of an application that visualizes longitudinal data from the Australian national electronic health record. Five professional psychiatrists took part in the assessment study.