Title |
Automated and unsupervised detection of malarial parasites in microscopic images
|
---|---|
Published in |
Malaria Journal, December 2011
|
DOI | 10.1186/1475-2875-10-364 |
Pubmed ID | |
Authors |
Yashasvi Purwar, Sirish L Shah, Gwen Clarke, Areej Almugairi, Atis Muehlenbachs |
Abstract |
Malaria is a serious infectious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard. However due to the number of steps required in manual assessment, this diagnostic method is time consuming (leading to late diagnosis) and prone to human error (leading to erroneous diagnosis), even in experienced hands. The focus of this study is to develop a robust, unsupervised and sensitive malaria screening technique with low material cost and one that has an advantage over other techniques in that it minimizes human reliance and is, therefore, more consistent in applying diagnostic criteria. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 1 | 1% |
Unknown | 96 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 19 | 20% |
Student > Master | 15 | 15% |
Student > Bachelor | 11 | 11% |
Student > Doctoral Student | 5 | 5% |
Lecturer | 4 | 4% |
Other | 19 | 20% |
Unknown | 24 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 22 | 23% |
Computer Science | 15 | 15% |
Medicine and Dentistry | 11 | 11% |
Agricultural and Biological Sciences | 6 | 6% |
Biochemistry, Genetics and Molecular Biology | 3 | 3% |
Other | 13 | 13% |
Unknown | 27 | 28% |