TY - GEN
T1 - Linguistic hedges fuzzy feature selection for differential diagnosis of erythemato-squamous diseases
AU - Azar, Ahmad Taher
AU - El-Said, Shaimaa A.
AU - Balas, Valentina Emilia
AU - Olariu, Teodora
PY - 2013
Y1 - 2013
N2 - The differential diagnosis of erythemato-squamous diseases is a real challenge in dermatology. In diagnosing of these diseases, a biopsy is vital. However, unfortunately these diseases share many histopathological features, as well. Another difficulty for the differential diagnosis is that one disease may show the features of another disease at the beginning stage and may have the characteristic features at the following stages. In this paper, a new Feature Selection based on Linguistic Hedges Neural-Fuzzy classifier is presented for the diagnosis of erythemato-squamous diseases. The performance evaluation of this system is estimated by using four training-test partition models: 50-50%, 60-40%, 70-30% and 80-20%. The highest classification accuracy of 95.7746% was achieved for 80-20% training-test partition using 3 clusters and 18 fuzzy rules, 93.820% for 50-50% training-test partition using 3 clusters and 18 fuzzy rules, 92.5234% for 70-30% training-test partition using 5 clusters and 30 fuzzy rules, and 91.6084% for 60-40% training-test partition using 6 clusters and 36 fuzzy rules. Therefore, 80-20% training-test partition using 3 clusters and 18 fuzzy rules are the best classification accuracy with RMSE of 6.5139e-013. This research demonstrated that the proposed method can be used for reducing the dimension of feature space and can be used to obtain fast automatic diagnostic systems for other diseases.
AB - The differential diagnosis of erythemato-squamous diseases is a real challenge in dermatology. In diagnosing of these diseases, a biopsy is vital. However, unfortunately these diseases share many histopathological features, as well. Another difficulty for the differential diagnosis is that one disease may show the features of another disease at the beginning stage and may have the characteristic features at the following stages. In this paper, a new Feature Selection based on Linguistic Hedges Neural-Fuzzy classifier is presented for the diagnosis of erythemato-squamous diseases. The performance evaluation of this system is estimated by using four training-test partition models: 50-50%, 60-40%, 70-30% and 80-20%. The highest classification accuracy of 95.7746% was achieved for 80-20% training-test partition using 3 clusters and 18 fuzzy rules, 93.820% for 50-50% training-test partition using 3 clusters and 18 fuzzy rules, 92.5234% for 70-30% training-test partition using 5 clusters and 30 fuzzy rules, and 91.6084% for 60-40% training-test partition using 6 clusters and 36 fuzzy rules. Therefore, 80-20% training-test partition using 3 clusters and 18 fuzzy rules are the best classification accuracy with RMSE of 6.5139e-013. This research demonstrated that the proposed method can be used for reducing the dimension of feature space and can be used to obtain fast automatic diagnostic systems for other diseases.
KW - Erythemato-Squamous Diseases
KW - Feature selection (FS)
KW - Linguistic Hedge (LH)
KW - Soft Computing
KW - Takagi-Sugeno-Kang (TSK) fuzzy inference system
UR - http://www.scopus.com/inward/record.url?scp=84872848288&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33941-7_43
DO - 10.1007/978-3-642-33941-7_43
M3 - Conference contribution
AN - SCOPUS:84872848288
SN - 9783642339400
T3 - Advances in Intelligent Systems and Computing
SP - 487
EP - 500
BT - Soft Computing Applications - Proceedings of the 5th International Workshop Soft Computing Applications, SOFA 2012
PB - Springer Verlag
T2 - 5th International Workshop on Soft Computing Applications, SOFA 2012
Y2 - 22 August 2012 through 24 August 2012
ER -