# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
## 1 Introduction  
Today, image recognition is becoming more and more important in areas such as facial identification, card registration number reading, searching for anomalies in medical photos, and many more, with significant improvements noticed in this field. Thus, the present article has been prepared to order and summarise the state of the art.  
Over the past 20 years, image processing technologies have seen significantly increased capabilities in relation to improved quality of recorded images, extension of the recorded spectrum, and greater computing power on the part of digital devices. The development of new image processing methods is important as well. The most popular of these is the use of Artificial Neural Networks (ANNs), which are analysed in this work.  
ANNs are among the artificial intelligence technologies that has been dynamically developing since the 1950s. ANNs are based the concept of the perceptron (1958). Theirdevelopment has been enabled by backpropagation algorithms and multilayer architectures, which allow for application in increasingly complex tasks. From the beginning of the 21 st century, the increase in computing power and the availability of large datasets has allowed for great progress in the field of deep learning, as expressed by architectures such as Convolutional Neural Networks (LeNet, AlexNet, VGG, ResNet) [1, 2, 3].  
To date, neural networks have been used for image recognition, segmentation, reconstruction, and classification of image data as well as in non-standard spectral bands such as thermal, infrared, and X-ray images. In medical imaging, neural networks have enabled automatic disease detection (e.g., detection of neoplastic lesions), tissue recognition, and segmentation [4, 5]. In the security sector, these technologies have been used for recognizing people in low-light conditions [6] as well as in weapon detection [7], and intelligent systems for monitoring critical infrastructure [8], among others.  
The main properties of neural networks include the ability to automatically extract features, flexibility in modelling nonlinear dependencies, and the ability to transfer trained networks between different problems (transfer learning). In recent years, optimisation of algorithms and dedicated hardware systems have made it possible to implement neural networks on embedded devices and to operate them in real time [9]. However, there are continuing challenges, including high demand for training data, sensitivity to interference and variability in the input data, and difficulty in interpreting model decisions.  
The purpose of this paper is to review the technological advances and key applications of neural networks in image processing over the last two decades (2005-2024), particularly thermal, infrared, and X-ray imaging.  
Section 2 presents the method used to select articles and their division according to the purposes of the proposed solution, then describes the current state-of-the-art. This is followed by a summary and analysis of the collected results in Section 3. A critical evaluation taking into account both the research and knowledge gaps is presented in Section 4. Finally, the full findings are synthesised in the last Section 5.