Title
Computer-aided diagnosis of thyroid malignancy using an artificial immune system classification algorithm.
Abstract
The diagnosis of thyroid malignancy by fine needle aspiration (FNA) examination has been proven to show wide variations of sensitivity and specificity. This paper proposes the utilization of a computer-aided diagnosis system based on a supervised classification algorithm from the artificial immune systems to assist the task of thyroid malignancy diagnosis. The core of the proposed algorithm is the so-called BoxCells, which are defined as parallelepipeds in the feature space. Properly defined operators act on the BoxCells in order to convert them into individual, elementary classifiers. The proposed algorithm is applied on FNA data from 2016 subjects with verified diagnosis and has exhibited average specificity higher than 99%, 90% sensitivity, and 98.5% accuracy. Furthermore, 24% of the cases that are characterized as "suspicious" by FNA and are histologically proven nonmalignancies have been classified correctly.
Year
DOI
Venue
2009
10.1109/TITB.2008.926990
IEEE Transactions on Information Technology in Biomedicine
Keywords
Field
DocType
supervised classification algorithm,computer-aided diagnosis system,average specificity,fna data,histologically proven nonmalignancies,so-called boxcells,proposed algorithm,thyroid malignancy diagnosis,artificial immune system classification,thyroid malignancy,artificial immune system,computer-aided diagnosis,artificial intelligence,ais,educational technology,feature selection,classification,feature extraction,classification algorithms,cancer,feature space,artificial immune systems,immune system
Fine-needle aspiration,Feature vector,Artificial immune system,Feature selection,Computer science,Computer-aided diagnosis,Algorithm,Feature extraction,Malignancy,Statistical classification
Journal
Volume
Issue
ISSN
13
5
1558-0032
Citations 
PageRank 
References 
3
0.41
13
Authors
5