An investigation of Bayes algorithm and neural networks for identifying the breast cancer
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 prima...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Thieme Medical and Scientific Publishers Pvt. Ltd.
2017-01-01
|
| Series: | Indian Journal of Medical and Paediatric Oncology |
| Subjects: | |
| Online Access: | http://www.ijmpo.org/article.asp?issn=0971-5851;year=2017;volume=38;issue=3;spage=340;epage=344;aulast=Udayakumar |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850226809587630080 |
|---|---|
| author | E Udayakumar S Santhi P Vetrivelan |
| author_facet | E Udayakumar S Santhi P Vetrivelan |
| author_sort | E Udayakumar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-efe75b5042614e16849ebdc02eef3ba4 |
| institution | OA Journals |
| issn | 0971-5851 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Thieme Medical and Scientific Publishers Pvt. Ltd. |
| record_format | Article |
| series | Indian Journal of Medical and Paediatric Oncology |
| spelling | doaj-art-efe75b5042614e16849ebdc02eef3ba42025-08-20T02:04:58ZengThieme Medical and Scientific Publishers Pvt. Ltd.Indian Journal of Medical and Paediatric Oncology0971-58512017-01-0138334034410.4103/ijmpo.ijmpo_127_17An investigation of Bayes algorithm and neural networks for identifying the breast cancerE UdayakumarS SanthiP VetrivelanContext: 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.http://www.ijmpo.org/article.asp?issn=0971-5851;year=2017;volume=38;issue=3;spage=340;epage=344;aulast=UdayakumarArtificial neural networkcomputer-aided diagnosisgray-level co-occurrence matrixmammogramregion of interest |
| spellingShingle | E Udayakumar S Santhi P Vetrivelan An investigation of Bayes algorithm and neural networks for identifying the breast cancer Indian Journal of Medical and Paediatric Oncology Artificial neural network computer-aided diagnosis gray-level co-occurrence matrix mammogram region of interest |
| title | An investigation of Bayes algorithm and neural networks for identifying the breast cancer |
| title_full | An investigation of Bayes algorithm and neural networks for identifying the breast cancer |
| title_fullStr | An investigation of Bayes algorithm and neural networks for identifying the breast cancer |
| title_full_unstemmed | An investigation of Bayes algorithm and neural networks for identifying the breast cancer |
| title_short | An investigation of Bayes algorithm and neural networks for identifying the breast cancer |
| title_sort | investigation of bayes algorithm and neural networks for identifying the breast cancer |
| topic | Artificial neural network computer-aided diagnosis gray-level co-occurrence matrix mammogram region of interest |
| url | http://www.ijmpo.org/article.asp?issn=0971-5851;year=2017;volume=38;issue=3;spage=340;epage=344;aulast=Udayakumar |
| work_keys_str_mv | AT eudayakumar aninvestigationofbayesalgorithmandneuralnetworksforidentifyingthebreastcancer AT ssanthi aninvestigationofbayesalgorithmandneuralnetworksforidentifyingthebreastcancer AT pvetrivelan aninvestigationofbayesalgorithmandneuralnetworksforidentifyingthebreastcancer AT eudayakumar investigationofbayesalgorithmandneuralnetworksforidentifyingthebreastcancer AT ssanthi investigationofbayesalgorithmandneuralnetworksforidentifyingthebreastcancer AT pvetrivelan investigationofbayesalgorithmandneuralnetworksforidentifyingthebreastcancer |