Higher order spectra analysis of breast thermograms for the automated identification of breast cancer
Breast cancer is a leading cancer affecting women worldwide. Mammography is a scanning procedure involvingX-rays of the breast. It causes discomfort and may cause high incidence of false negatives. Breast thermography is a new screening method of breast that helps in the early detection of cancer. I...
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/98535 http://hdl.handle.net/10220/16248 |
| _version_ | 1811681348835868672 |
|---|---|
| author | Acharya, U. Rajendra Ng, Eddie Yin-Kwee Sree, Subbhuraam Vinitha Chua, Chua Kuang Chattopadhyay, Subhagata |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Acharya, U. Rajendra Ng, Eddie Yin-Kwee Sree, Subbhuraam Vinitha Chua, Chua Kuang Chattopadhyay, Subhagata |
| author_sort | Acharya, U. Rajendra |
| collection | NTU |
| description | Breast cancer is a leading cancer affecting women worldwide. Mammography is a scanning procedure involvingX-rays of the breast. It causes discomfort and may cause high incidence of false negatives. Breast thermography is a new screening method of breast that helps in the early detection of cancer. It is a non-invasive imaging procedure that captures the infrared heat radiating off from the breast surface using an infrared camera. The main objective of this work is to evaluate the use of higher order spectral features extracted from thermograms in classifying normal and abnormal thermograms. For this purpose, we extracted five higher order spectral features and used them in a feed-forward artificial neural network (ANN) classifier and a support vector machine (SVM). Fifty thermograms (25 each of normal and abnormal) were used for analysis.SVM presented a good sensitivity of 76% and specificity of 84%, and theANN classifier demonstrated higher values of sensitivity (92%) and specificity (88%). The proposed system, therefore, shows great promise in automatic classification of normal and abnormal breast thermograms without the need for subjective interpretation. |
| first_indexed | 2024-10-01T03:39:31Z |
| format | Journal Article |
| id | ntu-10356/98535 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:39:31Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/985352020-03-07T13:22:18Z Higher order spectra analysis of breast thermograms for the automated identification of breast cancer Acharya, U. Rajendra Ng, Eddie Yin-Kwee Sree, Subbhuraam Vinitha Chua, Chua Kuang Chattopadhyay, Subhagata School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering Breast cancer is a leading cancer affecting women worldwide. Mammography is a scanning procedure involvingX-rays of the breast. It causes discomfort and may cause high incidence of false negatives. Breast thermography is a new screening method of breast that helps in the early detection of cancer. It is a non-invasive imaging procedure that captures the infrared heat radiating off from the breast surface using an infrared camera. The main objective of this work is to evaluate the use of higher order spectral features extracted from thermograms in classifying normal and abnormal thermograms. For this purpose, we extracted five higher order spectral features and used them in a feed-forward artificial neural network (ANN) classifier and a support vector machine (SVM). Fifty thermograms (25 each of normal and abnormal) were used for analysis.SVM presented a good sensitivity of 76% and specificity of 84%, and theANN classifier demonstrated higher values of sensitivity (92%) and specificity (88%). The proposed system, therefore, shows great promise in automatic classification of normal and abnormal breast thermograms without the need for subjective interpretation. 2013-10-04T02:56:39Z 2019-12-06T19:56:36Z 2013-10-04T02:56:39Z 2019-12-06T19:56:36Z 2012 2012 Journal Article Acharya, U. R., Ng, E. Y. K., Sree, S. V., Chua, C. K., & Chattopadhyay, S. (2012). Higher order spectra analysis of breast thermograms for the automated identification of breast cancer. Expert systems, 30(3). https://hdl.handle.net/10356/98535 http://hdl.handle.net/10220/16248 10.1111/j.1468-0394.2012.00654.x en Expert systems |
| spellingShingle | DRNTU::Engineering::Mechanical engineering Acharya, U. Rajendra Ng, Eddie Yin-Kwee Sree, Subbhuraam Vinitha Chua, Chua Kuang Chattopadhyay, Subhagata Higher order spectra analysis of breast thermograms for the automated identification of breast cancer |
| title | Higher order spectra analysis of breast thermograms for the automated identification of breast cancer |
| title_full | Higher order spectra analysis of breast thermograms for the automated identification of breast cancer |
| title_fullStr | Higher order spectra analysis of breast thermograms for the automated identification of breast cancer |
| title_full_unstemmed | Higher order spectra analysis of breast thermograms for the automated identification of breast cancer |
| title_short | Higher order spectra analysis of breast thermograms for the automated identification of breast cancer |
| title_sort | higher order spectra analysis of breast thermograms for the automated identification of breast cancer |
| topic | DRNTU::Engineering::Mechanical engineering |
| url | https://hdl.handle.net/10356/98535 http://hdl.handle.net/10220/16248 |
| work_keys_str_mv | AT acharyaurajendra higherorderspectraanalysisofbreastthermogramsfortheautomatedidentificationofbreastcancer AT ngeddieyinkwee higherorderspectraanalysisofbreastthermogramsfortheautomatedidentificationofbreastcancer AT sreesubbhuraamvinitha higherorderspectraanalysisofbreastthermogramsfortheautomatedidentificationofbreastcancer AT chuachuakuang higherorderspectraanalysisofbreastthermogramsfortheautomatedidentificationofbreastcancer AT chattopadhyaysubhagata higherorderspectraanalysisofbreastthermogramsfortheautomatedidentificationofbreastcancer |