# Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques
## 4 The Proposed Method
### Image Preprocessing Stage
#### 4.1.1 Noise Reduction
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. The CPU time consumed was computed o get the final classification accuracy each MLP activation function. 2.3 Scenario 3: Numbers of Layers and Neurons per Each Hidden Layer of the Best MLP Activation Function

The aim of this scenario is to further investigate the best MLP activation functions (obtained from Scenario 1). For the best MLP activation, we aim to test which parameters would give the highest classification rate. Specifically, we will investigate the Numbers of layers and neurons for each hidden layer. In this scenario, the following steps were followed. 1. We have tested the following parameters for the used neural network: 1. 3 hidden layers and a number of neurons 4,5 and 10 respectively, 2. 5 hidden layers and a number of neurons 4, 5 and 10 respectively and 3. 7 hidden layers and a number of neurons 4, 5 and 10 respectively. 2. For each of the above setup, a set of features were fixed for each experiment with the same chosen MLP activation function. 3. the CPU time consumed was computed to get the final classification accuracy for each (1.a, 1.b, 1.c)

#### 5.2.4 Scenario 4: Learning Rate of the Best MLP Activation Function

The aim of this scenario is to understand the impact of the learning rate of the best MLP activation functions (obtained from Scenario 1). We aim to test which value of the learning rate would give the highest classification rate. Specifically, we will investigate the effect of the numbers of hidden layers and their chosen neurons on the classification results. In this scenario, the following steps were followed. 1. We have tested the following parameters for the used neural network: 1. 3 hidden layers using number of neurons of 4, 5 and 10 respectively. 2. 5 hidden layers using number of neurons of 4, 5 and 10 respectively. 3. 7 hidden layers using number of neurons of 4, 5 and 10 respectively. 2. For each of the above setup, learning rate \(\eta\)= 1, 3, 5, 7, 9 was tested and the obtained results were recorded. 3. For each of the above setup, the set of features were fixed for each experiment with the same chosen MLP activation function. 4. The CPU time consumed was computed to get the final classification accuracy for each value of \(\eta\) set in Step 2. #### 5.2.5 Scenario 5: ELM Activation Functions

The aim of this scenario is to investigate the best ELM activation functions. The best will be determined based on the highest classification accuracy obtained. The following ELM activation functions will be examined: Sigmoid (sigmoid), Sine (sin), Triangular basis function (tribas), Hard Limit (hardlim), and Radial basis function (radbas). In this scenario, the following configurations were used. 1. Three layers of ELM were used: one for input, one hidden and one for the output. 2. These layers were constant for each ELM activation function. 3. The set of features were fixed for each ELM activation function. 4. This experiment was repeated for each ELM activation function above. #### 5.2.6 Scenario 6: Training Time of ELM Activation Functions

This scenario is designed to understand the required training time for each ELM activation function during the classification process. In this scenario, the following steps were followed. 1. Three layers of ELM were used: one for input, one hidden and one for the output)
2. These layers were constant for each ELM activation function. 3. The set of features were fixed for each ELM activation function. 4. This experiment was repeated for each ELM activation function above
5. The CPU time consumed was computed to get the final classification accuracy each ELM activation function. ## 6 Results and Discussions

In this section, the results of the experiments run under all the scenarios above will be reported and discussed. It will first present MLP results and then the ELM ones.