Abstract
Breast thermography is having a higher potential in detecting breast abnormalities due to high vascular indications beneath the abnormal regions of the breast. Hence, a thorough analysis of the features pertaining to the classification of these thermograms is necessary to emphasize the efficiency of the pattern classification. Image processing techniques such as feature extraction and pattern classification is applied to the acquired breast database. In comparison with the frontal views, the lateral view thermograms show better performance when classified with the pattern classifiers. The result shows 87% accuracy using KNN for the lateral view of breast thermograms.
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