Early Breast Cancer Detection in Thermogram Images using Supervised and Unsupervised Algorithms

In this paper we compare a highly accurate supervised to an unsupervised technique that uses breast thermal images with the aim of assisting physicians in early detection of breast cancer.
First, we segmented the images and determined the region of interest. Then, 23 features that included statistical, morphological, frequency domain, histogram and gray-level co-occurrence matrix based features were extracted from the segmented right and left breasts. To achieve the best features, feature selection methods such as minimum redundancy and maximum relevance, sequential forward selection, sequential backward selection, sequential floating forward selection, sequential floating backward selection, and genetic algorithm were used. Contrast, energy, Euler number, and kurtosis were marked as effective features.
The selected features were evaluated by fuzzy C-means clustering as the unsupervised method and compared with the AdaBoost supervised classifier which has been previously studied. As reported, fuzzy C-means clustering with a mean accuracy of 75% can be suitable for unsupervised techniques.
Fuzzy C-means clustering can be a suitable unsupervised technique to determine suspicious areas in thermal images compared to AdaBoost as the supervised technique with a mean accuracy of 88%.