TY - GEN
T1 - Quantum hybrid technique for feature selection in classification of cervical cells
AU - Iliyasu, Abdullah M.
AU - Fatichah, Chastine
AU - Salama, Ahmed S.
AU - She, Jinhua
N1 - Publisher Copyright:
© 2016, Fuji Technology Press. All rights reserved.
PY - 2016
Y1 - 2016
N2 - A quantum hybrid technique (QHT) that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO) method with the intuitionistic rationality inherent to traditional fuzzy k-nearest neighbours (Fuzzy k-NN) algorithm is proposed for efficient feature selection and classification of cells in cervical smeared (CS) images. Using a dataset of almost 1000 images, the proposed QHT technique drastically reduces the number of cell features, which leads to an average cell classification accuracy of 90%. This is an average of 7% improvement in the accuracy obtainable without the QPSO feature selection of the proposed QHT technique. With additional modifications, the proposed technique could prove useful in cervical cancer detection and diagnosis.
AB - A quantum hybrid technique (QHT) that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO) method with the intuitionistic rationality inherent to traditional fuzzy k-nearest neighbours (Fuzzy k-NN) algorithm is proposed for efficient feature selection and classification of cells in cervical smeared (CS) images. Using a dataset of almost 1000 images, the proposed QHT technique drastically reduces the number of cell features, which leads to an average cell classification accuracy of 90%. This is an average of 7% improvement in the accuracy obtainable without the QPSO feature selection of the proposed QHT technique. With additional modifications, the proposed technique could prove useful in cervical cancer detection and diagnosis.
KW - Cervical cancer
KW - Computational intelligence
KW - Fuzzy K-NN
KW - Hybrid intelligent techniques
KW - Medical image processing
KW - QPSO
UR - http://www.scopus.com/inward/record.url?scp=84997794594&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84997794594
T3 - ISCIIA 2016 - 7th International Symposium on Computational Intelligence and Industrial Applications
BT - ISCIIA 2016 - 7th International Symposium on Computational Intelligence and Industrial Applications
PB - Fuji Technology Press
T2 - 7th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2016
Y2 - 3 November 2016 through 6 November 2016
ER -
