## 7 Post-Processing Images
### Breast Thermograms
The authors reported that a random forest algorithm coupled with principal component analysis yields an accuracy of 95.45% in classifying healthy and unhealthy breasts. The performance of two different feature selection methods, namely, Least Absolute Shrinkage and Selection Operator (LASSO) Regression and Adaptive LASSO Regression, was assessed by [PERSON] et al. [108] when extracting texture characteristics from the breast thermograms. The extracted set of features was classified using support vector machineswith different kernel functions. The authors found that better classification accuracy is obtained when the support vector machine is supported by Adaptive LASSO Regression. [PERSON] and [PERSON] [109] suggested a breast cancer detection system involving a vision-based transformer used for image segmentation and four different convolutional neural network models employed for classification problems, such as EfficientNet-B7, ResNet-50, VGG-16. and DenseNet-201. The authors stated that the best accuracy and sensitivity results were reached using the ResNet-50 model. A hybrid methodology based on unsupervised and supervised machine learning techniques for analyzing dynamic infrared images was suggested by [PERSON] et al. [110]. Their methodology consists of three main steps. Each recorded image was first divided into a grid of squared pixels, and for each of them, the highest temperature was observed, leading to a temperature time series. Then the \(k\)-means clustering algorithm was applied to the set of the obtained time series. After that, clustering validation indices were applied to evaluate the clustering results, and the obtained values were then selected by feature selection algorithms. Finally, the obtained features were fed to different classifier models, such as K-Star, Bayes Net, multi-layer perceptron, decision table, and random forest classification. Although their methodology is able to identify cases of possible cancer with high accuracy, it does not allow the abnormality to be localized in the breast. [PERSON] and [PERSON] [111] applied five image segmentation methods to extract the hottest blood vessel patterns from benchmark breast thermograms available in a public data set. The authors concluded that the particle swarm optimization algorithm and multi-seed region-growing technique yield the best segmentation results. [PERSON] et al. [112] suggested a diagnostic tool based on automatic segmentation of breast thermograms. This automatic segmentation was achieved by feeding a deep learning algorithm with 170 infrared breast images. Moreover, to extract relevant features from the segmented thermograms, the authors performed textural evaluation and vascular network analysis. These features were then used as inputs in supervised learning algorithm-based classifiers in order to distinguish between normal and abnormal thermograms. The authors reported very encouraging results for both segmentation and classification. A deep convolutional neural network with transfer learning was applied by [PERSON] et al. [113] to automatically classify breast thermograms as normal and abnormal. In particular, they tested the neural network with a group of 311 female subjects. Moreover, two scenarios were considered in the analysis: a balanced class distribution and a typical screening cohort with a low prevalence of abnormal thermograms. The authors reported sensitivity values of 92.3% and 84.6% for the two scenarios, respectively, promoting the use of IR thermography as an adjunct method for breast cancer screening.