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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
› Author Affiliations Financial support and sponsorship Nil.
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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


Publication History

Article published online:
04 July 2021

© 2017. Indian Society of Medical and Paediatric Oncology. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/.)

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