Detection of Breast Cancer from Thermography Images
Suganthi L1, Nirmala K2, Shaalu Sree B.N3, Pavithra S4, Yashvantha N5
1Suganthi L, Department of Biomedical Engineering, SSN college of Engineering, Chennai, India.
2Nirmala K, Department of Biomedical Engineering, SSN college of Engineering, Chennai, India.
3Shaalu Sree B.N, Department of Biomedical Engineering, SSN college of Engineering, Chennai, India.
4Pavithra S, Department of Biomedical Engineering, SSN college of Engineering, Chennai, India.
5Yashvantha N, Department of Biomedical Engineering, SSN college of Engineering, Chennai, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 677-682 | Volume-8 Issue-6, August 2019. | Retrieval Number: F7945088619/2019©BEIESP | DOI: 10.35940/ijeat.F7945.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Breast cancer is now the most common cancer in most cities in India, and the second most common in rural areas. Early detection of breast cancer by systematic evaluation of the individual may improve survival rate. Infrared thermography one of the imaging technique that produce high resolution infrared images shows the heat pattern based on the temperature changes in breast with respect to tshe progression of the cancer cells. Increased metabolic activity and the blood flow due to the multiplication of cancer cells induces more heat on the skin layer which are captured by the thermal camera to produce the thermal images. This paper discuss on the real time image processing algorithm to detect the presence of cancer from the acquired thermal images. The methodology includes the preprocessing the acquired image and segmenting the region of interest, extracting the features from the segmented image followed by feature selection and classification. From the results, it is inferred that ANN classifiers yields better classification accuracy of 92% and minimum error rate (0.08) in K-means segmentation method when compared with SVM, KNN classifiers.
Keywords: ANN classifier, CLAHE, IR Thermography, K- means clustering, KNN classifier, Otsu thresholding, SVM.