A Novel Approach of Mathematical Theory of Shape and Neuro-Fuzzy Based Diagnostic Analysis of Cervical Cancer
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This study aims to detect the abnormal growth of tissue in cervix region for diagnosis of cervical cancer using Pap test of patients. The proposed methodology classifies cervical cancer for pattern recognition either benign or malignant stages using shape and neuro-fuzzy based diagnostic model. In this experiment, firstly the authors segment Pap smear images of cervical cells using fuzzy c-means clustering algorithm and shape theory to classify them according to the presence of abnormality of the cells. Secondly the features extraction process is performed in the part of nucleus and cytoplasm on the squamous and glandular cells and the authors used input variables such as cytoplasm area (CA), cytoplasm circularity (CC), nucleus area (NA), nucleus circularity (NC), nucleus-cytoplasm ratio (NCR), and maximum nucleus brightness (MNB) in fuzzy tools and used fuzzy rules to evaluate the cervical cancer risk status as an output variable. The proposed neuro-fuzzy network system was developed for early detection of cervical cancer. A neural network was trained with 15-Pap image datasets where Levenberg–Marquardt(LM) a feed-forward back-propagation algorithm was used to get the status of the cervical cancer. Out of 15 samples database, 11 data set for training, 2 data set for validation and 2 data set for test were used in the ANN classification system. The presented fuzzy expert system(FES) successfully identified the presence of cervical cancer in the Pap smear images using the extracted features and the use of neuro-fuzzy system(NFS) for the identification of cervical cancer at the early stages and achieve a satisfactory performance with 100% accuracy.
KeywordsCervical cancer Pap smear images segmentation Features extraction Shape theory Neuro-fuzzy classification system
We acknowledge with thanks the help and a cooperation we received from our colleges at Dumkal Institute of Engineering & Technology, Murshidabad, West Bengal, India and Indian Statistical Institute, Kolkata, India.
This study was funded by us.
Compliance with Ethical Standards
Conflict of Interest Statement
The authors declare that they have no conflict of interest.
Research Involving Human Participants and/or Animals
This article does not contain any studies with human participants or animals performed by any of the authors.
- 1.Jantzen J, Dounias, G (2006) Analysis of PAP smear image data. Proc. of Nature Inspired Smart Information System, NISIS 2006, pp 1–10Google Scholar
- 2.Majumder DD, Bhattacharya M (1998a) A new shape based technique for classification and registration: application to multimodal medical images. Int J of Image Processing and Communication 4(3–4):45–70Google Scholar
- 3.Sreedevi MT, Usha BS, Sandya S (2012) Pap smear image based detection of cervical cancer. Int J Comput Appl 45(20):0975–8887Google Scholar
- 4.Shah Sunny S et al (2016) Current technologies and recent developments for screening of HPV-associated cervical and orpharyngeal cancers. Cancers 8(85):1–27Google Scholar
- 5.Banarjee DK, Parui SK, Majumder DD (1994) A shape metric for 3-D objects. Indian Journal Pure Appl Math 25(1&2):95–111Google Scholar
- 6.Majumder DD (2015) Physics, chemistry and biology of nanoscale bio-technology-need for a unified framework for nanoarchitectures, DNA structures and nano-computing. EKHOMANJARI: a magazine on Science and Culture, BKC College, Kolkata, pp 13–32Google Scholar
- 10.Leica Microsystems, http://www.leica-microsystems.com/
- 11.Mahanta LB, Nath CK, Karan S, Nath DC, Majumder DD, Sharma JD (2011) Fuzzy mathematical and shape theoretic approach to cervical cell classification. Int J Comput Appl (0975–8887), 36, (6):1–5Google Scholar
- 12.Gallegos-Funes FJ, Gomez-Mayora ME, Lopez-Bonilla JL, Cruz-Santiago R (2009) Rank M- type radial basis function (RMRBF) neural network for pap smear microscopic image classification. Apeiron 16(4):542–554Google Scholar
- 13.Rosidi B, Jalil N, Pista NM, Ismail LH, Mengko ETL (2011) Classification of cervical cells based on labeled colour intensity distribution. International Journal of Biology and Biomedical Engineering 5(4):230–238Google Scholar
- 14.Talukdar J, Nath CK, Talukdar PH (2013) Fuzzy clustering based image segmentation of pap smear images of cervical cancer cell using FCM algorithm. Int. Journal of Engineering and Innovative Technology(IJEIT) 3(1):460–462Google Scholar
- 16.Majumder DD (1995) A study on a mathematical theory of shapes in relation to pattern recognition and computer vision. Indian Journal of Theoretical Physics 43(4):19–30Google Scholar
- 18.Byriel J (1999) Neuro-fuzzy classification of cells in Cervical smears, M.Sc Thesis, Technical University of Denmark, 1999Google Scholar
- 21.Wen C, Ming-Yu L, Horng-Jyh H, Yen-Chem H, Chien-Hung C, Kun-His T, Chi-Hung H (2009) Automatic segmentation of abnormal cell nuclei from microscopic image analysis for cervical cancer screening. 2009 IEEE 3rd International Conference on Nano/Molecular Medicine and Engineering, tainan, 18–21 Oct 2009Google Scholar