An Investigation of Bayes Algorithm and Neural Networks for Identifying the Breast Cancer

E Udayakumar; S Santhi; P Vetrivelan

doi:10.4103/ijmpo.ijmpo_127_17

Indian Journal of Medical and Paediatric Oncology, Inhaltsverzeichnis

CC BY-NC-ND 4.0 · Indian J Med Paediatr Oncol 2017; 38(03): 340-344
DOI: 10.4103/ijmpo.ijmpo_127_17

Original Article

An Investigation of Bayes Algorithm and Neural Networks for Identifying the Breast Cancer

E Udayakumar

Department of ECE, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, Tamil Nadu

,

S Santhi

Department of ECE, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, Tamil Nadu

,

P Vetrivelan

Department of ECE, PSG Institute of Technology and Applied Research, Coimbatore, Tamil Nadu

› Institutsangaben

Abstract

als PDF herunterladen

Abstract

Context: Breast cancer is a biggest threat to women. X-ray mammography is the most effective method for early detection and screening of breast cancer. It is a tough challenge for the radiologist in reading mammography since it does not provide consistent result every time. Aim: To improve the primary sign of this disease, computer-aided diagnosis schemes have been developed. Using monitor, digital images of mammography are displayed and they can be lightened or darkened before they are printed on the film. Time factor is important to identify the abnormality in body such as breast cancer and lung cancer. Hence, to detect the tissues and treatment stages, image-processing techniques are improved in several medical areas. In this project, using low-level preprocessing techniques and image segmentation, the breast cancer detection is done. Methods: With the help of Bayes algorithm and neural networks (NNs), the type of the mammogram and stages is identified. For segmentation process, region-growing algorithm is used, which helps to find the affected portion, i.e., region of interest. Gray-level co-occurrence matrix (GLCM) and texture feature are used for feature extraction. Results: Bayes algorithm is used for probability of identification, whereas NNs is used to reduce the probability level from 0–1000 to 0–1 in case of classification.

Keywords

Artificial neural network - computer-aided diagnosis - gray-level co-occurrence matrix - mammogram - region of interest

PDF (1791 kb)

Referenzen

References
1 Udayakumar E, Santhi S, Vetrivelan P. TB screening using SVM and CBC techniques. Curr Pediatr Res Allied Acad 2017;21:338-42.
2 Udayakumar E, Vetrivelan P. PAPR reduction for OQAM/OFDM signals by using neural networks. Int J Appl Eng Res 2015;10:30292-7.
3 Mencattini A, Salmeri M, Lojacono R, Frigerio M, Caselli F. Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing. IEEE Trans Instrum Meas 2008;6:1-12.
4 Balleyguier C, Ayady S, Nguyen KV, Vanel D, Dromain C, Sigal R. BIRADS classification in mammography. Eur J Radiol 2007;61:192:4.
5 Babbs CF, Liu S, Delp EJ. Multiresolution detection of speculated lesions in digital mammograms. IEEE Trans Image Process 2001;10:874-84.
6 Guliato D, Rangayyan RM, Carvalho JD, Santiago SA. Polygonal modelling of contours of breast tumours with the preservation of spicules. IEEE Trans Biomed Eng 2008;55:14-20.
7 Guliato D, Rangayyan RM, Carvalho JD, Santiago SA. Improving neural networks by preventing co-adaptation of feature detectors. IEEE Trans Image Process 2012;50:1-18.
8 Gravel P, Beaudoin G, De Guise JA. A method for modeling noise in medical images. IEEE Trans Med Imaging 2004;23:1221-32.
9 Mencattini A, Salmeri M, Caselli F, Sciunzi B, Lojacono R. Subband variance computation of homoscedastic additive noise in discrete dyadic wavelet transform. Int J Wavelets Multiresolut Inf Process 2008;6:1-12.
10 Haralick R, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973;3:610-21.
11 Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143:29-36.
12 Mencattini A, Rabottino G, Salicone S, Salmeri M. Uncertainty modeling and propagation through rFVs for the assessment of CADx systems in digital mammography. IEEE Trans Instrum Meas 2010;59:27-38.
13 Mohsin Jadoon M, Mohsin Jadoon M, Qianni Zhang, Ihsan Ul Haq, Adeel Jadoon, Abdul Basit, et al. Classification of mammograms for breast cancer detection based on curvelet transform and multi-layer perceptron. Biomed Res Allied Acad 2017; 28:4311-5.
14 Yogadinesh S, Oswalt Manoj S, Srihari K, Rajesh S, Devendran N, Ayyaparaja K, et al. Certain investigation of identify the new rules and accuracy using SVM algorithm. Middle East J Sci Res 2015;23:2074-80.
15 Udayakumar E, Santhi S, Gowrishankar R, Ramesh C, Gowthaman T. Region growing image segmentation for newborn brain MRI. Biotechnology: An Indian Journal Trade Sci Inc 2016;12:1-8.