1. Introduction
One of the most serious health issues that might have devastating effects for women nowadays is breast cancer. Changes in the cell genome, hormonal malfunction, family history, hormone therapy, lifestyle characteristics, and unfavorable life practices are potential risk factors and causes [
1]. Breast cancer is a complex illness, the progression of which is linked to alterations in a cell’s DNA brought on by environmental factors and hormones [
2]. It is regarded as one of the illnesses that kills more women than any other. The rate of recovery varies depending on the stage of the disease when it is diagnosed. Therefore, early diagnosis is vital, such that the tumor is likely to be treated at earlier stages.
There are many cancer diagnosis techniques, and mammography is the gold-standard. Breast tissue conditions including density, past surgery, lactation, breast implantation, and hormones, which should be in a normal state during diagnosis, limit the accuracy of mammography, which is an X-ray image of the breast. One adverse effect of this technique is that there could be harmful radiation, which is why mammography isn’t recommended more than twice a year [
3,
4,
5,
6,
7,
8].
Another method for cancer diagnosis and breast screening is ultrasound. To determine if a lump is a solid tumor or a cyst filled with fluids, an ultrasound is advised as a preliminary test. Younger people can benefit from it because their mammary glands have a denser structure. However, the expertise of the medical professional doing the test determines whether ultrasonography diagnosis will be successful (note: its accuracy is a function of volume to mass ratio) [
3,
4,
5,
6,
7,
8].
Furthermore, a widely used method of breast cancer diagnosis and screening is MRI (magnetic resonance imaging). The most reliable and accurate way to diagnose a tumor or breast cancer is via MRI scans [
3,
4,
5,
6,
7,
8]. However, currently, this procedure is the most expensive and is only available in major, well-equipped hospitals. F-FDG PET/CT is an emerging detection technique that combines PET and CT, which has synergistic advantages over PET or CT alone and minimizes their individual limitations. However, due to excessive cost and limited availability, F-FDG PET/CT application is severely constrained. PET itself is also a rather expensive technology, and neither PET scanners nor the cyclotrons required to create isotopes for PETs are generally accessible. For the overall evaluation of breast lesions, SPECT imaging has a higher diagnostic value than mammography, has been extensively validated, and has a high sensitivity. However, SPECT is much more expensive and less widely available than other techniques [
3,
4,
5,
6,
7,
8].
Finally, there is a method called contrast-enhanced spectral mammography (CESM), which can be used to detect breast cancer and provides low-energy mammographic images comparable to standard digital mammography, as well as a post-contrast recombined image to evaluate tumor neovascularity, like magnetic resonance imaging (MRI). This technique, however, has limitations, such as the usage of an iodinated contrast agent, and increased radiation exposure [
7].
Thus, every technique has its own advantages and disadvantages. The main disadvantages of the considered methods are that they are not practical for regular mass screening frequently, and some of them have limited access for people who live in remote areas of a country.
Thermography is one of the non-invasive and affordable techniques for routine and bulk screening [
5,
6]. It is common knowledge that many health problems can be accurately detected by a person’s body temperature. Blood perfusion, metabolic rate, and ambient temperature are just a few examples of the variables that affect how hot or cold the body is. Thermography could detect any temperature abnormality in the body, such as a tumor, as most tumors cause temperature changes in the surrounding tissue [
5,
6,
7,
8].
Thermography is a type of imaging that uses infrared (IR) light to create colored images of temperature distributions. According to the studies [
8] conducted in this field, the surface of a breast with cancerous tissues has a higher temperature profile than the surrounding region, and abnormalities can be discovered through thermography. The diagnosis of thermography is reliant on qualitative principles and human judgment, such as the asymmetry of two breasts, hyper-hermetic patterns, and atypical vascular patterns, even though thermography has straightforward functioning principles [
9]. By examining the temperature distribution and randomly matching the temperature profiles at several locations, quantitative information is often manually derived. With the quick advancement of computer technology, computer-aided tools can be used to help with the diagnosis by supporting the interpretation of thermal images, creating better breast models, and automatically identifying the locations and sizes of tumors, as well as other unique characteristics of breast tissues [
10].
The convolutional neural network (CNN), a deep-learning neural network of neurons with learnable weights and biases, is one newly discovered technique for recognizing images. The practice of identifying images based on their visual content is known as image classification. Recognizing breast thermograms with a predefined label is a crucial step in the learning process for neural networks. This is referred to as supervised learning. Without any human expert intervention, a diagnostic tool can be built using CNN and CNN-based models to classify “healthy” and “sick” types of thermograms as diagnosed by doctors. This study examines various CNN-based models with varying parameters and develops an efficient CNN model for binary classification using breast thermograms.
In the recent developments in the usage of state-of-the-art and transfer learning for medical images, several studies have shown results in breast cancer thermography. For instance, in the study by Zuluaga-Gomez [
11] they achieved an accuracy of 92% by using CNN with fifty-seven patients available data. Additionally, research by Torres-Galvan [
12] demonstrated an accuracy of 91.2% by exploiting the VGG-16 model, with 173 patients in total. 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.