A study and analysis of hybrid intelligent techniques for breast cancer detection using breast thermograms

Abstract

The growing incidence and mortality rate of breast cancer draw the attention of the researchers to develop a technique for improving the survival rate of the cancer patients. Medical infrared thermography (MIT) with sensitivity 90 % has proved itself as a safe and promising method for early breast cancer detection. Moreover, an abnormal breast thermogram can signify breast pathology. The accurate classification and diagnosis of these breast thermograms is one of the major problem in decision making for treatments, which leads to the utilization of hybrid intelligent system in breast thermogram classification. Hybrid intelligent system plays a vital role in survival prediction of a breast cancer patient, and it is highly significant in decision making for treatments and medications. The primary objective of a hybrid intelligent system is to take the advantages of its constituent models and at the same time lessen their limitations. This chapter is an attempt to highlight the reliability of infrared breast thermography and hybrid intelligent system in breast cancer detection and diagnosis. A detailed overview of infrared breast thermography including its principles and role in early breast cancer detection is described here. Several research works are carried out by various researchers to identify the breast pathology from breast thermograms by using hybrid intelligent techniques which include extraction and analysis of several statistical features. A study of research works related to feature extraction and classification of breast thermograms using various types of hybrid classifiers is also included in this chapter.