# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
## 2 Literature Review Methodology and State-of-the-Art
### State-of-the-Art: A Technology Review from 2005 to 2024
#### 2.3.2 Thermal Imaging  
A separate problem is the processing of a thermal image. The input material is much different from that obtained in visible light because it operates in infrared light [6; 58; 59; 60; 61; 62; 63; 64; 65; 66]. Thus, ANNs pretrained on pictures prepared in daylight are not sufficient. Moreover, the resolution of the thermal imaging is lower than that of the classical image.  
The most frequently investigated with thermal images is related to medical issues [58; 59; 60; 61; 65; 67; 68; 69; 70; 71]. In this scope, the main goal is to detect different kinds of disease. The spectrum of application is wide and starts from as general a problem as detecting sick newborns on thermal images [70]. During the COVID-19 pandemic, it was very important to detect potential ill people in a contactless manner [67; 68]. In addition, thermal images were used to detect the sick even if when wearing masks [67]. Moreover, observing persons' behaviour, especially coughing and shortness of breath, was helpful in detecting COVID-19.  
However, detecting infectious diseases is not the only application of thermal images. They can also be used to recognise rheumatoid arthritis [59; 65], breast cancer [58], peripheral arterial disease [69], or diabetic foot ulcers [60; 71]. In the latter case, thermal images can also be used to classify the stage of disease progression. It is helpful to choose the most suitable treatment.  
Despite the fact that thermal image processing can provide a lot of information, there is still space for further improvements. This is achieved by connecting this information with data taken from other sources. One of the most obvious is the combination of thermal and classical images, which can be used to calibrate important points [67] or to recognise persons or objects in visibility and invisibility light [6; 66]. In [72], the authors presented the addition of sensors to increase the safety of a space surrounding mining machines. Furthermore, adding an RFID detector to a drone can allow detection of a friend or enemy on a battlefield [73]. For these reasons, different methods of machine learning such as linear regression, decision trees, random forest, neural network, XGBoost, LightGBM, and the fuzzy c-means segmentation algorithm are applied [62; 64; 65; 66; 74; 75]. Their amount is also increasing because as input data there are not only static images but also thermal camera video [68; 73; 76].  
Thermal imaging is also a useful method to increase the accuracy of classifying a person's emotions. For example, using face thermography makes emotion detection 10% better than without it [63]. It could support emotional communication with autistic children, where this kind of cooperation is difficult [77]. Recognition could also be concerned with person identification. In [62], it was presented that the thermal facial image could be used for identification. It is especially important if the light conditions are not optimal [6]. In addition, in the security system the ability to detect a human in an open area when light is very weak is crucial [73; 76]. If necessary, protection could be extended for human detection [73; 76], person identification [62; 64; 6], or weapon detection [7]. The latter is also an example of object detection from thermal images. This skill makes it possible to apply this technology in the identification of electrical equipment [66] and faults [74]. For all of these reasons, thermal image processing should be performed in real time [7; 62; 63; 70; 72; 73; 74; 75], especially if they are used to protect people working close to machines in a mine [72], pedestriansnear autonomous vehicles, even if the images are in low resolution [75] or to monitor the condition of working machines [66; 74].