# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
## 5 Conclusions  
In the last two decades, the application of neural networks in imaging technologies has developed rapidly, including thermal, infrared, and X-ray imaging. The literature review confirms that methods based on deep networks, such as Convolutional Neural Networks (CNNs), have contributed to significant improvements in the quality of image detection, segmentation, and classification in varied fields from medicine to engineering and agriculture to public safety. However, as presented in Table 4, this has been particularly pronounced in the last decade. The growth in importance is observed in almost every field, especially in image processing (43%) and object recognition (46%). This topic is being taken up by scientists from all over the world, with the most active in the following locations: China, India, the United Kingdom, and the United States.  
A key property of ANNs is their ability to automatically extract features and detect complex patterns even in low-resolution or highly noisy images. This enables automatic diagnosis of diseases (e.g., cancers based on mammographic images or ischemic changes in the diabetic foot on thermal images), detection of people and objects in low-light conditions, or identification of the presence of hidden threats such as weapons. For this reason, in recent years research has focused mainly on the following areas:  
1. Object recognition
2. Person security and detection
3. Road sign recognition
4. Respiratory disease detection.  
Despite significant progress, many gaps are still open. These include the need to obtain large, representative, and well-described training datasets or issues related to energy efficiency and implementation in embedded systems and real-time systems as well as the Internet of Things (IoT). An important branch of development will be hybrid applications combining images of many types (e.g., combining data from IR, visible, and X-ray imaging), which will allow for better use of the potential of neural networks in classification and detection tasks. Filling the gaps will therefore focus on the following categories:  
1. Detection of various diseases, especially rare ones
2. Development of expert systems
3. Improvement of security systems
4. Improvement of autonomous systems.  
Filling these gaps should be one of the priorities in both basic and application research.  
In summary, neural networks are an important tool in image analysis systems from various areas of life and economy. The prospects for their development include further increasing the efficiency of models, reducing the demand for data and energy, and implementing them on mobile platforms and devices with limited computing power.  
In light of the above, it can be said that neural networks will play an increasingly important role in the development of modern imaging technologies, contributing to increased automation, improved diagnostics and safety, and reduced operating costs of many systems.  
Conceptualization, G.W.-J.; methodology, G.W.-J.; software, L.P.; validation, J.W.-J., L.P. and L.C.; formal analysis, L.P.; investigation, L.P.; resources, L.P.; data curation, L.C.; writing--original draft preparation, L.C.; writing--review and editing, L.C.; visualization, L.P.; supervision, J.W.-J.; project administration, J.W.-J., L.P. and L.C.; funding acquisition, J.W.-J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflicts of interest.