People with epilepsy were (PWE) reported to have poorer employment rate. However, the methodologies used differ greatly from one study to another, making global comparison difficult. We aimed to determine the employment rate of PWE globally using a unified definition of employment rate and to summarize the reported positive and negative factors affecting employability in PWE, using a systematic review.
Matched MeSH terms: Databases, Factual/statistics & numerical data
The demand for automatically classification of medical X-ray images is rising faster than ever. In this paper, an approach is presented to gain high accuracy rate for those classes of medical database with high ratio of intraclass variability and interclass similarities. The classification framework was constructed via annotation using the following three techniques: annotation by binary classification, annotation by probabilistic latent semantic analysis, and annotation using top similar images. Next, final annotation was constructed by applying ranking similarity on annotated keywords made by each technique. The final annotation keywords were then divided into three levels according to the body region, specific bone structure in body region as well as imaging direction. Different weights were given to each level of the keywords; they are then used to calculate the weightage for each category of medical images based on their ground truth annotation. The weightage computed from the generated annotation of query image was compared with the weightage of each category of medical images, and then the query image would be assigned to the category with closest weightage to the query image. The average accuracy rate reported is 87.5 %.