COMPARATIVE STUDY OF HEURISTIC-BASED SUPPORT VECTOR MACHINE AND NEURAL NETWORK FOR THERMOGRAM BREAST CANCER DETECTION WITH ENTROPY FEATURES

Abstract

Thermography is a noncontact, noninvasive imaging technology that is commonly utilized in the medical profession. As early identification of cancer is critical, the computer-assisted method can enhance the diagnosis rate, curing, and survival of cancer patients. Early diagnosis is one of the major essential steps in decreasing the health and socioeconomic consequences of this condition, given the high cost of therapy and the large prevalence of afflicted people. Mammography is currently the majorly utilized procedure for detecting breast cancer. Yet, owing to the low contrast that occurs from a thick breast, mammography is not advised for young women, and alternate methods must be investigated. This work plans to develop a comparative evaluation of two well-performing heuristic-based expert systems for detecting thermogram breast cancer. The thermogram images are taken from the standard DMR dataset. Then, the given images are transferred to the pre-processing stage. Here, the input thermogram images are accomplished by contrast enhancement and mean filtering. Then the Gradient Vector Flow Snakes (GVFS) model is adopted for breast segmentation, and Optimized Fuzzy [Formula: see text]-Means Clustering (OFCM) is developed for abnormality segmentation. From the segmented region of interest, the entropy-based features are acquired. In the classification phase, the “Heuristic-based Support Vector Machine” (HSVM) and “Heuristic-based Neural Network” (HNN) are introduced, which diagnose the breast cancer-affected images. The modifications on SVM and NN are extended by the Oppositional Improvement-based Tunicate Swarm Algorithm (OI-TSA). Furthermore, the suggested models are compared to the traditional SVM and NN classifiers, as well as other classifiers, to validate their competitive performance. From the results, the better accuracy and precision of the designed OI-TSA–HNN model are found to be 96% and 98.4%, respectively. Therefore, the findings confirm that the offered approach shows effectiveness in thermogram breast cancer detection.