Multi-level of feature extraction and classification for X-Ray medical image
There has been a rise in demand for digitized medical images over the last two decades. Medical images' pivotal role in surgical planning is also an essential source of information for diseases and as medical reference as well as for the purpose of research and training....
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
Institute of Advanced Engineering and Science (IAES)
2018
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/63248/ http://irep.iium.edu.my/63248/ http://irep.iium.edu.my/63248/ http://irep.iium.edu.my/63248/13/63248%20Multi-Level%20of%20Feature%20Extraction%20and%20Classification%20for%20X-Ray%20%20SCOPUS.pdf http://irep.iium.edu.my/63248/19/63248_Multi-Level%20of%20Feature%20Extraction%20and%20Classification%20for%20X-Ray_article.pdf |
| Summary: | There has been a rise in demand for digitized medical images over the last
two decades. Medical images' pivotal role in surgical planning is also an
essential source of information for diseases and as medical reference as well
as for the purpose of research and training. Therefore, effective techniques
for medical image retrieval and classification are required to provide accurate
search through substantial amount of images in a timely manner. Given the
amount of images that are required to deal with, it is a non-viable practice to
manually annotate these medical images. Additionally, retrieving and
indexing them with image visual feature cannot capture high level of
semantic concepts, which are necessary for accurate retrieval and effective
classification of medical images. Therefore, an automatic mechanism is
required to address these limitations. Addressing this, this study formulated
an effective classification for X-ray medical images using different feature
extractions and classification techniques. Specifically, this study proposed
pertinent feature extraction algorithm for X-ray medical images and
determined machine learning methods for automatic X-ray medical image
classification. This study also evaluated different image features (chiefly
global, local, and combined) and classifiers. Consequently, the obtained
results from this study improved results obtained from previous related
studies. |
|---|