# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
## 4 Discussion
### Neural Networks and Platforms  
To summarise the review in Section 2.3 in terms of the implementation of neural networks on various devices, the following groups can be distinguished:  
1. Edge devices: * Applications: Fast recognition (e.g., number of people, simple monitoring, mobile robots, data acquisition directly from sensors). * Solutions: Lightweight CNN models (e.g., small, optimised networks), simplified machine learning algorithms, classifiers with low resource requirements. * Hardware architectures: microcontrollers, DSP (Digital Signal Processor), low-power ARM processors, dedicated PCB systems (e.g., energy metres, simple IoT sensors), often with ZigBee or WiFi wireless communication. * Example: Reading physical metres using a CMOS image sensor + DSP + neural network, sending results through ZigBee to the concentrator [55].
2. Embedded industrial systems: * Applications: Advanced monitoring systems, autonomous robots, real-time control, industrial inspection systems (e.g., production lines, mobile robots, autonomous vehicles). * Solutions: Medium-sized CNN models, sensor fusion (e.g., IR, cameras, radar), low-latency processing, sometimes recurrent networks, or Spiking Neural Networks (SNNs). * Hardware architectures: FPGA, System-on-Chip (SoC) systems with their own hardware acceleration for AI, sometimes embedded GPU (e.g., Nvidia Jetson). * Example: FPGA + CMOS for vehicle parking and orientation systems [48], mobile robots using data from multiple sensors and neural networks [79; 80; 81; 82; 83; 84; 85; 86].
3. Stand-alone Desktop Computers: * Applications: Advanced image processing, data mining, scientific experiments, simulations, algorithm development and testing, more complex analyses (e.g., segmentation, multiclass classification, transfer learning). * Solutions: Full-scale CNN architectures (e.g., ResNet, U-Net), GAN for image quality improvement, advanced autoencoder networks, classic MLP networks for signal analysis. * Hardware architectures: PC-class CPU, GPU (Nvidia, AMD), workstations, standard laboratory equipment. * Example: Training and testing networks in the Matlab environment on a PC with CPU/GPU [12].
4. Cloud computing: * Applications: Complex operations on large datasets, large-scale modelling and prediction, remote medical diagnostics, big data analysis, large-scale deep learning, model servicing for clients. * Solutions: Very large deep networks (Deep CNN, GAN, transformers), high-performance transfer learning techniques, hybrid and multi-type models, training and inference on distributed clusters. * Hardware architectures: GPU clusters, TPU (Google), distributed cloud environments (AWS, Azure, Google Cloud), containerisation (Docker, Kubernetes). * Example: Medical applications using remotely trained and hosted models in the cloud for image data analysis (e.g., X-ray) [119; 120].
5. Supercomputers:* Applications: Training very large AI models, processing massive datasets from many sources (e.g., genetic projects, medical imaging studies), multiscale simulations, data mining from many sensors simultaneously.
* Solutions: Very complex deep networks, hybrid networks, advanced classifiers and segmenters, multi-stage simulations using many models.
* Hardware architectures: Multithreaded CPU+GPU/TPU clusters, dedicated AI supercomputers (e.g., El Capitan, Summit, Fugaku) (top500.org, accessed on 18 June 2025).
* Example: Analysis and training of networks for large-scale diagnostics, large-scale data fusion from multiple sources [119; 120].  
The above analysis shows that in edge devices more emphasis is placed on energy efficiency, simple design, and real-time operation. As the performance of platforms (desktop, cloud computing) increases, the scale, complexity, and demand for computing power increase as the algorithms become more complex and multi-layered. It can be seen that a significant part of the solutions described in the article can be scaled depending on need.