TY - JOUR
T1 - Cervical cancerous cell classification
T2 - opposition-based harmony search for deep feature selection
AU - Das, Nibaran
AU - Mandal, Bodhisatwa
AU - Santosh, Kc
AU - Shen, Linlin
AU - Chakraborty, Sukanta
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2023/11
Y1 - 2023/11
N2 - Over 500 K (per year) cervical cancer cases are reported with a high mortality rate (6–9%). Automatically detecting cervical cancer using the Computer-Aided Diagnosis (CAD) tool at an early stage is important since it leads to successful treatment as pathologists. In this paper, we propose a tool that classifies cervical cancer cases from Pap smear cytology images using deep features. The proposed tool constitutes a Convolutional Neural Network (CNN) and a metaheuristic evolutionary algorithm called Opposition-based Harmony Search Algorithm (O-bHSA) for deep feature section. These features are classified using standard classifiers: SVM, MLP, and KNN. On two different publicly available datasets: Pap smear and liquid-based cytology, the proposed tool outperforms not only seven well-known optimization algorithms but also state-of-the-art methods. Codes are publicly available on GitHub .
AB - Over 500 K (per year) cervical cancer cases are reported with a high mortality rate (6–9%). Automatically detecting cervical cancer using the Computer-Aided Diagnosis (CAD) tool at an early stage is important since it leads to successful treatment as pathologists. In this paper, we propose a tool that classifies cervical cancer cases from Pap smear cytology images using deep features. The proposed tool constitutes a Convolutional Neural Network (CNN) and a metaheuristic evolutionary algorithm called Opposition-based Harmony Search Algorithm (O-bHSA) for deep feature section. These features are classified using standard classifiers: SVM, MLP, and KNN. On two different publicly available datasets: Pap smear and liquid-based cytology, the proposed tool outperforms not only seven well-known optimization algorithms but also state-of-the-art methods. Codes are publicly available on GitHub .
KW - CNN
KW - Cervical cancer
KW - Deep features
KW - Opposition-based harmony search
UR - http://www.scopus.com/inward/record.url?scp=85162009270&partnerID=8YFLogxK
U2 - 10.1007/s13042-023-01872-z
DO - 10.1007/s13042-023-01872-z
M3 - Article
AN - SCOPUS:85162009270
SN - 1868-8071
VL - 14
SP - 3911
EP - 3922
JO - International Journal of Machine Learning and Cybernetics
JF - International Journal of Machine Learning and Cybernetics
IS - 11
ER -
