Optimal feature extraction for the classification of medical images
Year: 2008
Type: report
Abstract: Dementia is significant loss of intellectual abilities such as memory capacity, severe enough to interfere with social or occupational functioning. The most common types of dementia are: Alzheimer's disease (AD), Lewy body dementia (LBD), and frontotemporal dementia (FTD). In 2008, there are currently 29.8 million people with dementia, with the number expected to be 81.1 million by 2050. The classification of FDG-PET in patients with dementia might not be an easy task. Some dementia diseases have similar disease patterns which lead to the misdiagnosis of these diseases. Nowadays, images are visually evaluated by an expert reader and this process is not entirely quantitative or reproducible. Even the experts can have up to 20% misclassification. Automated diagnosis by pattern recognition can produce quantitative and reproducible results, but if training data comes from clinical routine, it may produce less accurate results to discriminate similar disease patterns. Hence, the work of this thesis aims to find an optimal subset of features, for a given training data set, which improves the classification of FDG-PET in patients with suspected dementia.The presented work describes some methods that found effective in improving the classification problem at hand. These methods consist of: (i) feature extraction, (ii) feature ranking, (iii) classifier learning with balanced data, (iv) feature selection. The results demonstrate the possibility to improve the accuracy of pair-wise classification for the most common dementia diseases with an excellent accuracy(in less time) by these methods.
Authors: Ahmed Serag, Fabian Wenzel, Frank Thiele, Samantha Young
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2137828935