# Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques
## 2 Related Work  
[PERSON] et al. [14] proposed a breast cancer detection method combining thermography and mammography. The results of this method showed that thermogram-based detection only achieved 83% of sensitivity, mammogram-based detection gave 90% while combined thermogram with mammogram achieved 95% sensitivity.  
[PERSON] et al. [15] proposed a feature extraction method known as Block Variance (BV). This method depends on the local texture. They then used it for thermogram breast cancer detection. In the classification phase, they used hybrid method of a gradient descent training rule and a feed-forward neural network. They evaluated their method using the public database, DMR but they used 100 images ( 40 malignant and 60 benign). The proposed system is evaluated using asymmetry analysis approach. The results showed that this system is good at classification accuracy (less than 0.1 false-positive rate) comparing to related work. However, this good result is achieved using a very small dataset (40 malignant and 60 benign).  
[PERSON] et al. [16] proposed a thermogram breast cancer detection based on classifying contours that are visible on thermogram images taking directly from the Brister device. The images are first classified based on their contours and then their attributes are computed. These attributes are then classified using four classifiers, namely Decision Tree, Naive Bayes, Random Forrest, and SVM, to evaluate which of them gives the best cancer detection results. This evaluation showed that the Random Forest classifier achieved the best results measured under sensitivity and specificity. However, this good result is achieved using a private dataset collected from the Brister device.  
[PERSON] et al. [17] suggested a thermogram-based method to detect breast cancer early. They extracted several statistical features including the energy, mean, homogeneity, and entropy from each image. They then used the SVM to detect the images with a cancer tumor. In the experiments, they used 50 thermograms (25 normal and 25 abnormal). The results showed that the SVM achieved specificity of 90.48% and sensitivity at 85.71%. A compression sensitivity results achieved by an expert radiologist showed that the proposed method is better by 8%. Although these are good results, they have been achieved using a small dataset (25 malignant and 25 benign). Thus the results cannot be generalized.  
[PERSON] et al. [1] suggested an automatic segmentation and then classification of normal and abnormal breast. The segmentation is based on an optimized Fast Fuzzy C-mean algorithm and Neutrosophic sets. Then SVM classifier is used to differentiate between normal or abnormal images (i.e., patients). The proposed method was evaluated using sensitivity and accuracy with the highest  
Figure 2: Typical breast cancer detection system.  
accuracy results at 88.41%. However, the dataset used in this evaluation was small (29 healthy and 34 malignant), thus the results cannot be generalized.  
[PERSON] et al. [18] proposed the use of the SVD to distinguish abnormal thermograms from the normal ones. Under the measurements of accuracy, specificity and sensitivity, the experimental results showed the proposed method achieved 98.00%. Although the dataset used (45 abnormal and 100 normal) was bigger than the ones used in [15; 17]. However, these results still can not be generalized with this small dataset.  
[PERSON] et al. [19] conducted a comparative study between Ensemble Bagged Trees and AdaBoost for the detection of breast cancer from thermograms. They utilized two type of features, spectral and spatial features, in the classification phase. The evaluation results showed that Ensemble Bagged Trees classifier is better than AdaBoost one. This evaluation was done based on the accuracy of 87%, sensitivity of 83% and specificity of 90.6%. However, the size of the used dataset was not given.