# 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 reported results of the accuracy, specificity and sensitivity are the average of ten runs of the proposed method. ### The Results of Scenario 1

The accuracy of training and testing of the different activation functions are compared. All results of the activation functions are presented in \[F(y,z)=F(ln(l(v,w)))+F(ln(f(v,w)))\]1 The best results (accuracy of the trained and testeddata) were obtained using the \"tansig\" activation function. This best function (tansig) was then further evaluated in terms of specificity, sensitivity, the results 84% for specificity and 61.6% for sensitivity. ### The Results of Scenario 1: MLP Activation Functions. The time required for training of the different activation functions are compared. All results of activation functions are presented in \[F(y,z)=F(ln(l(v,w)))+F(ln(f(v,w)))\]2 The best results of the training time were obtained using the \"purlin\" activation function and it was 2.890241 s. Although this time is better than tansig's time but it is quite a small amount (0.195924 s). The current advance in computer speed (supercomputer and GPU) would make this processing time irrelevant. ### The Results of Scenario 3

The accuracy of training and testing of different number of layers and neurons used for each hidden layer of \"tansig\" activation function are compared. All results of accuracy are presented in \[F(y,z)=F(ln(l(v,w)))+F(ln(f(v,w)))\]3 From this table, it can be noticed that the best result (accuracy at 82.20% with training time 15.811615s) was obtained when the number of hidden layers and neurons were 7 and 10 respectively

The results of this scenario showed a positive relationship between the training time and the number of neurons. The training time increases when the number of neurons increases. From Table 3, we found that when the number of neurons was 10, the training times increased. Similarly, the results showed a positive relationship between the training time and the accuracy of training. The accuracy of training increases if the training time increases. From Table 3, the accuracy of training was the best result when the training times increased. \begin{table}
\begin{tabular}{c c} \hline \hline
**Activation Functions** & **Accuracy** \\ \hline
**tansig** & 0.7424 \\
**logistic** & 0.6996 \\
**poslin** & 0.615 \\
**satlins** & 0.6076 \\
**satlin** & 0.6142 \\
**purlin** & 0.6466 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results of Scenario 1: MLP Activation Functions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Activation Functions** & **Training Time** \\ \hline
**tansig** & 3.086165 \\
**logistic** & 3.473274 \\
**poslin** & 3.182216 \\
**satlins** & 3.047807 \\
**satlin** & 2.957490 \\
**purlin** & 2.890241 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of Scenario 2: MLP Training Time. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Neurons = 4**} & \multicolumn{2}{c}{**Neurons = 10**} & \multicolumn{2}{c}{**Neurons = 5**} \\ \cline{2-7}
**No. of Layers** & **Accuracy** & **Time** & **Accuracy** & **Time** & **Accuracy** & **Time** \\ \hline
**3** & 0.6892 & 3.29759 s & 0.7834 & 5.615519 s & 0.7010 & 3.416063 s \\
**5** & 0.6684 & 4.014458 s & 0.8042 & 10.3971355 s & 0.6788 & 4.145014 s \\
**7** & 0.6958 & 3.838878 s & 0.8220 & 15.811615 s & 0.7025 & 5.98080 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of Scenario 3: The accuracy and training time for different configurations of “tansig” activation function. ### The Results of Scenario 4

Based on the results given in scenario 1, the activation function \"tansig\" was further tested with different learning rates and compared when the number of hidden layers was 7 and the number of neurons was 10.