###### Abstract
In the current study, several techniques that are not used in those papers are applied and try to outperform the results of the previous studies. Another advancement in the field is the development of Bayesian Networks. Bayesian Networks (BN) or influence diagrams are knowledge representation schemes capable of expressing every type of knowledge: discrete or continuous, certain or uncertain. Technically speaking, BNs are probabilistic networks between statistical factors with additional information about their causal/influence interconnections. BNs are an artificial intelligence method with interpretability. The latter means that the diagnosis is not coming from a black box. The physician will be able to understand, which is the crucial factor that results in the specific decision. BNs are applied successfully to a large spectrum of different domains [13; 14; 15; 16; 17; 18]. Perhaps, the most outstanding performance and meaningful use of BNs is for medical diagnosis. Almost perfect diagnostics were demonstrated many years ago [19]. The advantage of BNs lies in the fact that since they are probabilistic networks that connect statistical factors (random variables), they are interpretable. BNs are a knowledge representation scheme that encapsulates certain and uncertain knowledge. Therefore, when the network is trained by data (supervised learning), the result is not the estimation of weights on various layers but conditional probabilities among meaningful statistical factors. Thus, there is transparency and interpretability for any kind of diagnosis or probabilistic inference with BNs. The aim of this paper is to assess several state-of-the-art neural network techniques for diagnosis from thermal images and compare their performances with those of the BNs. In addition, this study aims to demonstrate that by integrating BNs and CNN (or similar neural network algorithms), an expert system with remarkably high accuracy and, at the same time, interpretability with limited datasets can be generated. Furthermore, an automatic crop of the thermal images and automatic extraction of temperature-related features are implemented in the study. The latter is particularly important for BNs to achieve accuracy like CNNs. If the feature extraction is not appropriate, then BNs are not able to compete with CNNs. Therefore, this paper gives an answer to the obvious question, \"when is a feature extraction scheme from images acceptable, and when can it be used for an interpretable knowledge representation BN structure\"? The current paper focuses on developing CNNs based on a multi-source database without preprocessing for binary classification. Furthermore, the paper compares transfer learning methods with the baseline CNN model to develop an intelligent tool for breast cancer detection. The paper further discusses the concepts of transfer learning and studies different models with this concept. Then it introduces the idea of Bayesian Networks and the data used, its implementation details are described, and an integrated BN + CNN model is presented. Finally, the results of BN and the integrated BN + CNN models are presented and discussed. In the end, a conclusion and discussion are given.