# Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques
## 1 Introduction  
Breast Cancer (BC) is a big threat to women around the world causing high mortality to them. To increase survival rates, it is found that it is very effective to early detect breast cancer. Various approaches have been produced to achieve high survival rates. It is obvious that traditional techniques are time-consuming and error-prone as they require physicians and oncologists to use their experiences and naked eyes in diagnosis process. Therefore, high efficient and automated techniques detecting the breast lesions cells are needed [1]. There are four early signs of breast cancer. These signs are architectural distortion, mass, microcalcification, and breast asymmetries [2]. There are many medical imaging techniques aiming to detecting these signs. Examples of these techniques include mammography, magnetic resonance imaging (MRI), tomography, and ultrasound etc. [3]. These techniques usually produce images which are then analysed for recognizing benign or malignant patterns. The mammography technique is the most well-known screening technique for detecting breast abnormalities. However, in the case of women with dense breasts, mammograms technique doesnot work well because dense breasts may hide tumor cells [4; 5]. Consequently, mammography screening could lead to high false negative/positive rate of such patients. Another disadvantage of mammography is that imaging formation needs X-ray radiation. As reported in [6], this radiation is found to increase the chance for the patient to have the cancer in future. Thus, there is a need for another screening technique to address these limitations. Infrared-based (thermography) screening was found to be effecting in address these limitations. This thermography depends on monitoring the physiological changes in a woman's breast. From such physiological changes and before structural changes are done in the breast, breast cancer lesions can be detected in its early stage [7]. One of the most important advantages of thermography is that it can be used for women at early ages, women with breast implants and women with high breast densities [8]. Ohtre advantages are that thermography is safe ( non-ionizing and a non-invasive) and painless medical imaging technique [6]. The main idea of breast thermography techniques is that each object (the breast in our case) produces infrared radiation which measures the vascular heat radiated [9; 10]. It was found that the growing tumor has a higher metabolic percentage than the other surrounding area and associated rise in local vascularization [11]. Based on this discovery, it was proved, by asymmetric heat patterns, that there is a difference between abnormal and normal thermogram images [11]. Visual asymmetric heat patterns can be noticed in Figure 1. As can be seen in this figure, the left image shows a normal breast in which the heat distributions in the left and right breast are symmetric but asymmetric in the right image. Usually, physicians/oncologists look for such abnormalities and decide subjectively. However, it is not always possible to diagnose all types of abnormalities found in thermograms by just naked eyes. Automatic or semi-automatic techniques such as Computer-Aided Detection or Diagnosis (CAD/CADx) are beneficial as they can help physicians/oncologists on discovering more information on such abnormalities from medical images [7; 12]. A typical CAD system usually consist of six major phases as shown in Figure 2.  
This paper aims to conduct a comparative study between ELM and MLP classifiers which would give the best results in terms of performance and accuracy. To achieve this aim, a model is proposed as follow. Firstly, thermal images are pre-processed for noise reduction and enhancement. Secondly, they are segmented to get the tumor area. Thirdly, features of the tumor region are extracted to be used in the cancer detection phase (fourth phase) which is accomplished using ELM and MLP algorithms by which the tumor region was classified either benign or malign.