Cervical Cancer Classification from Pap Smear Images Using Modified Fuzzy C Means, PCA, and KNN

Abstract

Cervical cancer is having the second-highest mortality rate next to breast cancer among women in developing countries. Early detection of the abnormality is the only way to prevent morbidity. As the decision about the abnormality of the cell is made manually by the traditional Pap smear test – the clinical test conducted for the detection of cervical cancer is more prone to false-negative and false-positive cases. This paper presents a novel approach for the automatic detection of cervical cancer using modified fuzzy C-means, extracting the geometrical and texture features, Principal Component Analysis (PCA), and classification. Modified fuzzy C-means show promising results in segmenting the input image into meaningful regions even when there is uncertainty. PCA is being performed to reduce the dimensionality of the data set by maintaining only the uncorrelated features thereby reducing the processing time of the algorithm. The classification of the pap smear images into normal and abnormal cells is being done by K Nearest Neighbour (KNN) classification with k-fold cross-validation and the result obtained in the proposed method is being compared with Fine Gaussian SVM, Ensemble Bagged trees, and Linear Discriminant. The efficiency of the proposed method is measured by calculating minimum accuracy, maximum accuracy, average accuracy, sensitivity, specificity, F1-score, and precision. The experimental results of the proposed method show impressive results with minimum accuracy 94.15%, maximum accuracy 96.28%, average accuracy 94.86%, sensitivity 97.96%, specificity 83.65%, F1-score 96.87%, and precision 96.31% for threefold cross-validation.

KEYWORDS:

• Cervical cancer
• k-fold validation
• KNN
• Modified fuzzy C-means
• Pap smear
• Principal Component Analysis (PCA)

ACKNOWLEDGEMENTS

The authors are very much thankful to the management of PSNA College of Engineering and Technology for the valuable support while carrying out this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

N. Lavanya Devi

N Lavanya Devi has received 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 is pursuing 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 includes image processing, biometrics, etc. She is a life member of IETE (AM203209L). Corresponding author. E-mail: [email protected]

P. Thirumurugan

P Thirumurugan has received bachelor's degree in electronics and communication engineering from Anna University, Chennai. He received master's degree in applied electronics from Anna University, Chennai, Tamil Nadu, India. He has completed his PhD degree in Faculty of Information and Communication Engineering, Anna University, Chennai, Tamil Nadu, India. He is now working as professor in PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India. His research interest includes image processing and wireless sensor network. He is a life member of the Indian Society for Technical Education. He has published more than 40 articles in International Journals and presented many papers in National and International Conferences. He has been honoured as Best Teaching Faculty – Dr APJ Abdul Kalam Educational Trust award for his meritorious teaching interest and excellence in the year 2019. E-mail: [email protected]