TOWARD EFFECTIVE BREAST CANCER DETECTION IN THERMAL IMAGES USING EFFICIENT FEATURE SELECTION ALGORITHM AND FEATURE EXTRACTION METHODS

In this paper, an intelligent method is developed for improving the performance of the Computer-Aided Detection (CAD) system. The research objective is to improve the performance of the CAD system in Breast Cancer (BC) detection with high accuracy using thermal images. The research strategy is efficient using feature extraction, feature selection, classification and artificial intelligence methods. In the developed method, the features in the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) are extracted from images. The features are selected using the firefly feature selection algorithm. These selected features are observed to be relevant for the abnormality detection in healthy and unhealthy breasts. The [Formula: see text]-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision-Tree (D-Tree) classifiers are then applied to these features for the detection of malignancy in the breast. The breast thermograms of 200 subjects available at the Database for Mastology Research for breast research using InfraRed images, DMR-IR database, are considered for evaluation of our intelligent method. The results demonstrate that the accuracy is 98.8%, 81.5%, and 95%, the sensitivity is 99%, 83.15%, and 95.91%, and the specificity is 98.2%, 80%, and 94.11% when using SVM, kNN, and D-Tree classifier algorithm, respectively. This reveals the effectiveness of our intelligent method to improve the accuracy of the CAD system in the BC detection.