# Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques
## 4 The Proposed Method
### Image Preprocessing Stage
#### 4.1.1 Noise Reduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Paper/Criteria** & **Dataset Size** & **Public/Private Dataset** & **Classifiers** & **Accuracy** & **Specificity** & **Sensitivity** \\ \hline
**[PERSON]. [PERSON] [2009]** & – & Private & TH(1:5)scale & – & – & 95\% \\ \hline
**[PERSON] [2016]** &
\begin{tabular}{c} 40 malignant \\ 60 benign \\ \end{tabular} & Public(DMR) & FANN & 90\% & 85\% & 95\% \\ \hline
**[PERSON] [2016]** & \
\begin{tabular}{c} 325 examin. \\ \end{tabular} & Private &
\begin{tabular}{c} Naive Bayes \\ Decision Tree \\ \end{tabular} & – & – & – \\ \cline{3-6}  & & & SVM & R. Forrest & & \\ \hline
**[PERSON] [2010]** &
\begin{tabular}{c} 40 normal \\ 60 malignant \\ \end{tabular} & Private & SVM & 88.10\% & 90.48\% & 85.71\% \\ \hline
**[PERSON] [2015]** &
\begin{tabular}{c} 29 healthy \\ 34 malignant \\ \end{tabular} & Public(benchmark) & SVM & 92.06\% & – & – \\ \hline
**[PERSON] [2018]** &
\begin{tabular}{c} 70 abnormal \\ 50 normal \\ \end{tabular} & Private & SVM(Poly) & 98\% & 98\% & 98\% \\ \hline
**[PERSON] [2018]** & – & Public (DMR) &
\begin{tabular}{c} E. Bagg. Trees \\ AdaBoost \\ \end{tabular} & 87\% & 90.6\% & 83\% \\ \hline
**Our Solution** & \begin{tabular}{c} 705 normal \\ 200 benign \\ 440 malignant \\ \end{tabular} & Public (DMR-IR) & \
\begin{tabular}{c} MLP \\ ELM \\ \end{tabular} &
\begin{tabular}{c} 80.04\% \\ 99.10\% \\ \end{tabular} & 84\% & 61.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The comparison between the proposed work and the other related work. **Author Contributions:** All authors were evolved in funding acquisition. Data curation, M.W.A.E.-S.; Formal analysis, T.G. ; Investigation, T.G. and M.W.A.E.-S.; Methodology, M.W.A.E.-S. and T.G. ; Project administration, F.A. ; Software, M.W.A.E.-S.; Supervision, F.A. ; Writing-original draft, M.W.A.E.-S. and T.G. ; Writing-review & editing, T.G. All authors have read and agreed to the published version of the manuscript. **Funding:** The work and the contribution were supported by Research Center for engineering and applied sciences and College of Science at Zulfi City, Majmaah University, Saudi Arabia Project No. 38/80. **Acknowledgments:** The authors would like to thank the deanship of scientific research and Research Center for engineering and applied sciences, Majmaah University, Saudi Arabia, for their support and encouragement; also the authors would like to express our deep thanks to our College (College of Science at Zulfi City, Majmaah University, Saudi Arabia), Project No. 38/80. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

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