# 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
#### 2.3.1 Image Sensors
As a result, a system performs better and has more energy efficiency. The use of fuzzy logic to increase the sensitivity for a wider spectrum of signals was presented in [48]. The hybrid approach is not only used in this case. In expert systems where detection of skin disease is necessary, a connection of neural and Bayesian networks has been applied [4; 5]. Thus, diagnosis is better and treatment is more effective. Among the most important features is an image correction. The reasons could be different: dark environments, whole objects not being visible, sensor resolution lower than modern ones, or as in electrical charge tomography, the object of the image is not seen directly [4; 5; 31; 38; 40; 41; 49]. Using a neural network in image sensors allows for reconstructing an object during their runtime and gives better results than an interpolation [37; 40]. To strengthen this effect, sometimes information from other sensors is engaged [32; 45]. An image analysis is not concerned only around static pictures. Some devices are designed to detect movement. Comparison of changes in series of pictures is possible, but using neural networks gives better results [35]. Movement detection could be used, for example, to recognise workers at a construction site building [50], in particular to monitor them if they are in danger from the site of building machines. This is one of the reasons for which a time of execution of a detection should be more predictable. In [34], the authors presented how to make the network computation time more deterministic. In particular, this may play a crucial role in some applications related to autonomous vehicles. This segment is still growing and is not limited only to assist during parking [48], recognising items surrounding vehicles [51], or recognising road signs [52]. Despite these technologies growing separately, their combination could bring a new quality in this field. A key aspect is applying image sensors in production technologies. To reduce a cost of transport, solid particles flowing in a pipeline are very often used. To control this process, electrical charge tomography images are analysed [49]. Another example of image sensor usage with edge detection is control of the welding process. Due to the large number of variables that influence this process, it is a challenging job [53]. The quality of it is increased by adding signals from other detectors [54]. A standout feature added more and more frequently to image sensors is face recognition. Implementing it on a camera allows for saving energy with the same recognition quality [30]. Furthermore, for the pretrained neuron networks it is possible to use them to identify people wearing a surgical mask [46]. In addition, the identification based on fingerprints is improved [47]. Similar but heading in a different direction is tissue recognition [4]. In this case, the main goal is not identification of the person but a pressure ulcer. The use of neural networks in image sensors that enable text recognition has enabled the development of an additional to standard energy and water metres. Reading it, the module is able to send its value through the ZigBee network, which allows remote reading of the metre, which was not adapted for this [55; 56]. Image processing can also be used by wearable devices. For example, ref. [57] presented a system for eye tracking and real-time object recognition in various lighting conditions.