# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
## 3 Results  
To track changes in the studied area, the time period considered was divided into two decades: 2005-2014 and 2015-2024. Table 4 presents a summary of changes in subsequent decades, taking into account the division into document type, technology used, solution used, and research method used. To detect and assess the changes that occurred in these categories, a chi-square independence test was performed. The null hypothesis (\(H_{0}\)) assumed that there was no relationship between the publication period and the frequency of a given category. In contrast, the alternative hypothesis (\(H_{1}\)) is presented that there is a relationship between the period and the number of publications in a given category. Figure 2 shows the change in the publication of documents of different types. Despite the overall increase in the number of publications, the proportions between conference publications, press publications, and book chapters did not change significantly. Therefore, with a chi-square test value of \(\chi^{2}=1.8\) and three degrees of freedom, the \(p\)-value is equal to \(p=0.41\). This confirms the null hypothesis that the proportion between publication types did not change. The next group studied is applied technologies. A summary of the changes is presented in Figure 3. In general, an increase in the number of publications using each technology can be observed. However, the test value \(\chi^{2}=14.72\), which with three degrees of freedom gives \(p\)-value = 0.0. This means that the null hypothesis \(H_{0}\) should be rejected for this group and the alternative hypothesis \(H_{1}\) should be accepted. This means that statistically significant changes have occurred. In this case, it can be seen that a disproportionately large increase was observed within thermal imaging technology. This suggests that a significant number of new studies have occurred in this area. \begin{table}
\begin{tabular}{c c c} \hline
**Algorithm** & **Example Architectures** & **Purpose** \\ \hline Classic convolutional analysis (CNN) [16; 17; 23] & LeNet, AlexNet, VGGNet, ResNet & Image classification, automatic feature extraction \\ \hline Object detection [1] & YOLO, Faster R-CNN, SSD & Object detection, localization in real time \\ \hline Image segmentation [14] & U-Net, Mask R-CNN & Object segmentation, e.g., in medicine or technical applications \\ \hline Autoencoders/denoising algorithms [12] & Autoencoder, DnCNN & Noise removal from images, image reconstruction \\ \hline Sequence analysis (CNN + RNN/LSTM) [3] & CNN+RNN, CNN+LSTM & Temporal changes analysis, motion detection, gesture recognition \\ \hline Transfer learning [119] & ResNet, VGG, Inception (fine-tuning) & Adaptation to new data using pre-trained networks \\ \hline Multi-sensor fusion [120] & Hybrid networks, late fusion CNN & Combining data from different sensors (e.g., image + IR + radar) \\ \hline Super-resolutionand & SRGAN, EDSR & Increasing resolution and quality \\ enhancement [121; 122] & & of images \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of examples of neural network algorithms.