Support vector machine based methodology for classification of thermal images pertaining to breast cancer.

Breast cancer has been and continues to be a cause of major health concern for women. It is more prevalent in old age, but its incidence has increased in recent years in groups below 50 years old, as in India. According to the Indian Council of Medical Research (ICMR) 2020, 50% of all the cases are in the age group of 25-50 where the numbers are staggering and constantly rising. The increase in incidence over the years indicates an urging need for innovative approaches to enhance breast cancer detection early. Thermography is non-contact imaging modalities and has potential to detect breast cancer at an early stage. Though thermography has capable of detecting breast cancer early, the challenge lies in the interpretation of the breast thermograms with respect to features and subsequent analysis. The present work discusses image acquisition, image processing related pre-processing, segmentation, and feature extraction. The extracted features were analyzed using ANOVA (Analysis of variance) statistical analysis. Statistical analyses were done in order to find the appropriate feature on the whole and quadrant breast. Statistical analysis results clearly reveal existence of thermal symmetry for the healthy subjects (p value > .05) in both whole and quadrant breast regions. In the case of abnormal subjects, whole breast analyses revealed the significance (p value < .05) for features like mean, variance, standard deviation, kurtosis, skewness, entropy, energy, homogeneity and contrast whereas upper outer quadrant analyses showed significance for all above features except contrast. The well correlated features of upper outer quadrant and whole breast were given as input for the Support Vector Machine - Radial Basis Function (SVM - RBF) classifier with grid search method. The results revealed that whole breast analysis has achieved 92.86% accuracy and upper outer quadrant breast analysis has achieved 85.71% accuracy. The results clearly indicate the involvement of upper outer quadrant and whole breast in early detection of breast cancer using thermal imaging.

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Declaration of competing interest The authors declare that they do not have any financial conflicts of interest in this study.