## 3 Results
### Qualitative Synthesis  
#### 3.1.1 Breast Cancer  
Breast cancer diagnosis is the topic with the highest number of publications for the application of thermal imaging in conjunction with ML algorithms. [PERSON] et al. presented the first studies using ANN to detect breast malignancies, but with low performance metrics of ACC, SN and SP (58.5%, 54% and 67%) [15; 16]. These results were improved further along with a more elaborate approach focused on the use of biostatistical methods (SN = 81.2%, SP = 88.2%) [17]. The main author also collaborated in the work of [PERSON] et al. where fuzzy rules were implemented for the construction of hidden layers in a neural network, yielding good SN results (100%) in the proposed classification tasks [18]. The improvement of classification parameters when fuzzy-neural networks are selected in place of single neural networks was also confirmed by another author's research [19; 20]. [PERSON] et al. studied the number of ideal partitions in a fuzzy-rule based classification system for breast cancer, reaching ACC, SN and SP values of 97.95%, 93.10% and 99.15%, respectively, as the number of partitions increased [21; 22]. A fuzzy model based on C-means clustering by [PERSON] et al. showed accuracy values of 75% for the screening of suspicious breast areas, a lower  
Figure 1: Flow of information through the different phases of the systematic review [13]. performance when compared to the supervised AdaBoost algorithm developed in their previous work (88%) [23; 24]. Other strategies to improve neural networks' performance in breast cancer classification refer to the extraction of features with wavelet transform [25], or to numerical simulations conducted on various breast tissue composition models [26]. The preferred learner for breast cancer detection studies is based on Support Vector Machines (SVM). [PERSON] et al. studied the extraction of statistical and textural features in the curvelet domain, classifying 91% of instances correctly [27]. The use of Principle Component Analysis (PCA) in subsequent work to reduce the number of features, along with dynamic thermal image acquisition, led to lower values of ACC (83.3%) [28]. A similar approach, using only four clinically significant features, had previously achieved good performance metrics with a larger dataset (ACC = 88.10%, SN = 85.71%, SP = 90.48%) [29]. The Radial Basis Function (RBF) kernel appears to be the best option for the implementation of SVM in breast cancer research, as proven in [30; 31; 32; 33], as well as the use of textural features retrieved from thermograms, all with high performance metrics. [PERSON] et al. focused its work on the gathering of the best possible inputs that characterize healthy, benign and malignant cases through temperature and intensity analysis (ACC = 83.2%, SN = 85.5%, SP = 73.2%) [34]. SVM was the chosen learner after comparison with other AI algorithms [35]. [PERSON] et al. tried a different strategy, using local energy features of wavelet sub-bands after temperature matrix normalization to classify normal and abnormal breast thermograms with ACC = 91%, SN = 87.23% and SP = 94.34% with a SVM-Gaussian [8]. Apart from neural networks and support vector machines, studies with equally high classification metrics can be found using AdaBoost [36], Bayesian classifiers [37] and k-NN [38; 39] classifiers. With the goal of achieving the best possible outcome, some authors choose to test different AI learners, with SVM [40; 41], k-NN [42; 43], ANN [44; 45] and Decision tree [46] algorithms being the best options among the tested ones. The use of ensemble classifiers has been frequent in recent years. Most work of [PERSON] et al. is based on a pool of learners, which are as distinct as possible from each other, in order to reduce complexity and avoid redundant information during the classification stage [47; 48; 49].