Improved Cervical Cancer Classification by Weighted Majority Voting With Sampling

Lavanya Devi N; C N Savithri; M Jayanthi; S M Asha Banu

doi:10.5935/jetia.v12i59.3260

Authors

Lavanya Devi N Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India. https://orcid.org/0000-0002-3908-0162 (unauthenticated)
C N Savithri Department of Electronics and Communication Engineering, Sri Sairam Engineering College, Chennai, Tamil Nadu, India. http://orcid.org/0000-0001-6291-229X (unauthenticated)
M Jayanthi Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bengaluru, Karnataka, India. http://orcid.org/0000-0002-6510-6629 (unauthenticated)
S M Asha Banu Department of Electronics and Communication Engineering, Sri krishna College of Engineering and Technology, Kuniamuthur,Coimbatore, India

DOI:

https://doi.org/10.5935/jetia.v12i59.3260

Abstract

Developing countries have a high rate of cervical cancer due to the detection of cancer at the stage of regional spread of the disease rather than detection at the early stage. Early detection of the disease by a periodical pap smear test is the most effective way of reducing the rate of cervical cancer. This research paper shares a novel model for the automated classification of cervical cancer with weighted majority voting. Upsampling and downsampling are done to overcome the inefficiency due to an imbalance in the database. Size, statistical, intensity, and texture features are extracted from the modified fuzzy c-means segmented image. Principal Component Analysis (PCA) is done on the extracted features to rank and identify the most significant features. Supported Vector Machine (SVM), K Nearest Neighbour (KNN), and Naïve Bayes (NB) are tuned to the kernel function and a number of cross validations and are given as the input to the weighted majority voting system. The performance of the proposed method with up sampling, down sampling, and no sampling are compared, and it was found that the weighted majority voting with upsampling has the highest classification accuracy of 99%.

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Author Biographies

Lavanya Devi N, Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India.

Lavanya Devi N has received the Bachelor’s Degree in Electronics and Communication Engineering from Anna University, Chennai. She received Master’s degree in Communication Systems from Anna University, Chennai. She completed PhD degree in the Faculty of Information and Communication Engineering, Anna University, Chennai, Tamil Nadu. Currently she is working as Assistant Professor in the Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul. Her area of interest include image processing, biometrics etc. She is a life member of IETE and ISTE.
C N Savithri, Department of Electronics and Communication Engineering, Sri Sairam Engineering College, Chennai, Tamil Nadu, India.

C.N.Savithri is currently working as Associate Professor in the Department of Electronics and Communication Engineering, Sri Sai Ram Engineering College. She received her B.E Degree in Electronics & Communication Engineering from Madras University in 2002 and M.E degree in communication systems from Anna University in 2006. She completed her Ph.D under the faculty of Information and Communication, Anna University in the field of biomechanical modelling. Her research interest covers Machine Learning, Deep Learning, modeling and simulation, mechatronics and Biomedical Signal and image processing. She has published papers in International Journals and Conferences in the areas of biomedical signal and image processing employing machine learning algorithms to her credit. She is a Senior Member of IEEE, Life Member of ISTE, Member of IETE.
M Jayanthi, Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bengaluru, Karnataka, India.

M. Jayanthi is currently working as Associate Professor in Electronics and communication Engineering, New Horizon College of Engineering (Autonomous), Bangalore. She received the B.E Degree in Electronics and Communication Engineering from Bharathidasan University, in the year 2002 and M.E degree in Communication Systems from Mepco Schlenk Engineering college, Sivakasi, Tamil Nadu in the year 2006 and Ph.D. degree in Electrical and Electronics engineering, VTU University, Belagavi, India, 2019. Her research interests includes image processing, Digital Signal processing, Biomedical signal and image processing, IoT, and Machine Learning.She has authored or coauthored more than 45 refereed journal and conference papers. She can be contacted at email:jayanthim@newhorizonindia.edu
S M Asha Banu, Department of Electronics and Communication Engineering, Sri krishna College of Engineering and Technology, Kuniamuthur,Coimbatore, India

Dr S.M Asha Banu received B.E degree in Electronics and Communication Engineering from NPR Engineering College, Dindigul in the year 2014. She received her M.E degree in Communication Systems from SSN College of Engineering and Technology,affiliated to Anna University, Chennai in the year 2016. She completed her PhD degree in the stream of Information and Communication Engineering from Anna University, Chennai in the year 2023 in the Field of Antenna Design for Biomedical applications. She has totally 10 years of teaching experience 6 years of research experience. She is currently working as Assistant Professor in the department of ECE, Sri Krishna College of Engineering and Technology, Coimbatore. Her research interests include Communications especially in the Field of Antenna Systems.