# Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques
## 4 The Proposed Method
### Image Preprocessing Stage
#### 4.1.1 Noise Reduction
* **Entropy:** It is a statistical measure of randomness of an intensity image. It is used to represent the texture of a given image. Mathematically, an entropy of an image is computed as, \(sum(H.*(log)_{2}(h))\), where \(h\) denotes the histogram counts for the intensity image [13]. * **Edge-Based Contrast Measure (EBCM):** This is a feature which depends on the human perception and is very sensitive to edges. Mathematically, EBCM feature of an image, \(I\), is calculated as given in the following equation: \[ln(img(v,w))=ln(j(v,w))+ln(f(v,w))\]7 (16) \(C(h,q)\) is a contrast value for an image pixel located at \((h,q)\) and is calculated as \[ln(img(v,w))=ln(j(v,w))+ln(f(v,w))\]8 (17) where the mean gray level is: \[ln(img(v,w))=ln(j(v,w))+ln(f(v,w))\]9 (19)

### Classification Stage

Classification is the last stage in the breast cancer detection technique which includes two operations: training and testing. The extracted features are given inputs into the classification techniques to evaluate whether an given thermogram is normal, benign tumor, or malignant tumor. In this paper, two classification techniques (i.e., MLP and ELM) are applied to evaluate the performance and accuracy of the proposed method. In the MLP and ELM networks, the selection of the activation functions plays a substantial role in the performance of the network. Many studies have investigated special activation functions to solve different problems. So, in this paper, we investigated different activation functions in both MLP and ELM networks to evaluate which would give high accuracy and performance. The tested activation functions are: neuronal, sigmoid, logarithmic, hyperbolic tangent, exponential, and sinusoidal. The performances of the MLP and ELM networks are calculated based on the correct classification percentage. Figure 10 summarizes the proposed method. ## 5 Experiments

### Dataset Description and Experiments Setup

The dataset used in the evaluation of the proposed method is DMR-IR database [25]. It consists of thermogram images and it is made available for researchers to evaluate their thermogram-based breast

\[F(y,z)=F(ln(l(v,w)))+F(ln(f(v,w)))\]0 Flowchart of the Proposed Method. cancer detection proposals. Ii contains 1345 images: 705 normal, 200 benign tumors, and 440 malignant tumors. The patients are women between ages of 32 and 74 and thermal IR breast images are with size 640 \(\times\) 480 pixels. All database images and their annotations are confirmed by radiologists. In the evaluation of the proposed method, 1000 samples of these images are utilized to train the proposed method and the remaining 345 samples are utilized to test and validate the proposed method. All experiments were conducted on a laptop/PC Core i5-2400 CPU 3.10 GHz with 4.00 GB. The implementation was compiled using MATLAB R2015a under Windows 10. ### Experiments Scenarios

We designed several experimental scenarios to examine the best MLP and ELM activation functions and parameters giving the best results as well as the performance of MLP and ELM. #### 5.2.1 Scenario 1: MLP Activation Functions

The aim of this scenario is to investigate the best MLP activation functions. The best will be determined based on the highest classification accuracy obtained. The following MLP activation functions will be examined: saturating linear (satlin), linear (purelin), symmetric saturating linear (satlins), log-sigmoid (logsig), positive linear (poslin), and hyperbolic tangent sigmoid (tansig). In this scenario, the following steps were followed. 1. The MLP number of layers was one for input, one hidden and one for the output. 2. These layers were constant for each MLP activation function. 3. The set of features were fixed for each MLP activation function. 4. This experiment for each MLP activation function above. #### 5.2.2 Scenario 2: Training Time of MLP Activation Functions

This scenario is designed to understand the required training time for each MLP activation function during the classification process. In this scenario, the following steps were followed. 1.