TY - GEN
T1 - Image classification using evolving fuzzy inference systems
AU - Othman, Ahmed A.
AU - Tizhoosh, Hamid R.
PY - 2013
Y1 - 2013
N2 - Evolving fuzzy systems change by online updating of their parameters and structure; the number of fuzzy rules changes as long as there is new data. In literature, an evolving fuzzy system is mainly considered to be an unsupervised approach that builds and updates its clusters online as long as new data is available. In our previous works, we introduced a new supervised evolving fuzzy approach for segmenting medical images. In this paper, we demonstrate that this supervised evolving fuzzy approach can classify images. As an example we attempt to classify medical images based on their modalities. A set of features extracted from the image is used to train the fuzzy system with the modality class of the image as the fuzzy output. The proposed algorithm is applied to both ultrasound scans and magnetic reasoning images (MRI). The proposed algorithm is compared with the support vector machines (SVMs) and the K-nearest neighbour algorithm (KNN). The results show that evolving fuzzy systems can compete with well-establish clustering algorithms (and even surpass them) by delivering high classification rates.
AB - Evolving fuzzy systems change by online updating of their parameters and structure; the number of fuzzy rules changes as long as there is new data. In literature, an evolving fuzzy system is mainly considered to be an unsupervised approach that builds and updates its clusters online as long as new data is available. In our previous works, we introduced a new supervised evolving fuzzy approach for segmenting medical images. In this paper, we demonstrate that this supervised evolving fuzzy approach can classify images. As an example we attempt to classify medical images based on their modalities. A set of features extracted from the image is used to train the fuzzy system with the modality class of the image as the fuzzy output. The proposed algorithm is applied to both ultrasound scans and magnetic reasoning images (MRI). The proposed algorithm is compared with the support vector machines (SVMs) and the K-nearest neighbour algorithm (KNN). The results show that evolving fuzzy systems can compete with well-establish clustering algorithms (and even surpass them) by delivering high classification rates.
UR - http://www.scopus.com/inward/record.url?scp=84886528930&partnerID=8YFLogxK
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U2 - 10.1109/IFSA-NAFIPS.2013.6608612
DO - 10.1109/IFSA-NAFIPS.2013.6608612
M3 - Conference contribution
AN - SCOPUS:84886528930
SN - 9781479903474
T3 - Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013
SP - 1435
EP - 1438
BT - Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013
T2 - 9th Joint World Congress on Fuzzy Systems and NAFIPS Annual Meeting, IFSA/NAFIPS 2013
Y2 - 24 June 2013 through 28 June 2013
ER -