Abstract
The object of research is to parallelize the learning process of artificial neural networks to automate the procedure of medical image analysis using the Python programming language, PyTorch framework and Compute Unified Device Architecture (CUDA) technology. The operation of this framework is based on the Define-by-Run model. The analysis of the available cloud technologies for realization of the task and the analysis of algorithms of learning of artificial neural networks is carried out. A modified U-Net architecture from the MedicalTorch library was used. The purpose of its application was the need for a network that can effectively learn with small data sets, as in the field of medicine one of the most problematic places is the availability of large datasets, due to the requirements for data confidentiality of this nature. The resulting information system is able to implement the tasks set before it, contains the most user-friendly interface and all the necessary tools to simplify and automate the process of visualization and analysis of data. The efficiency of neural network learning with the help of the central processor (CPU) and with the help of the graphic processor (GPU) with the use of CUDA technologies is compared. Cloud technology was used in the study. Google Colab and Microsoft Azure were considered among cloud services. Colab was first used to build a prototype. Therefore, the Azure service was used to effectively teach the finished architecture of the artificial neural network. Measurements were performed using cloud technologies in both services. The Adam optimizer was used to learn the model. CPU duration measurements were also measured to assess the acceleration of CUDA technology. An estimate of the acceleration obtained through the use of GPU computing and cloud technologies was implemented. CPU duration measurements were also measured to assess the acceleration of CUDA technology. The model developed during the research showed satisfactory results according to the metrics of Jaccard and Dyce in solving the problem. A key factor in the success of this study was cloud computing services.
Highlights
Artificial intelligence (AI) methods are increasingly used to analyze source texts and understand their meaning, requirements management, specification development, design, code generation, verification, testing, quality assessment, reusability identification, problem solving on parallel systems, etc. medical systems, advise doctors in emergency situations, robotic manipulators to perform precise actions during surgical operations
The development of frameworks based on the Python programming language has greatly simplified the development process
Having measurements for different angles of arrival of X-rays, the computer program can reconstruct the values of the absorption coefficients μ at each point of the scanned section
Summary
Artificial intelligence (AI) methods are increasingly used to analyze source texts and understand their meaning, requirements management, specification development, design, code generation, verification, testing, quality assessment, reusability identification, problem solving on parallel systems, etc. medical systems, advise doctors in emergency situations, robotic manipulators to perform precise actions during surgical operations. Due to the complexity of calculations and large amounts of data, it was possible to effectively learn artificial neural networks (ANNs) only with the help of computers with high power. The situation has changed only recently, in particular with the advent of Compute Unified Device Architecture (CUDA) technology. This made it possible to learn ANNs using the relatively cheap and powerful graphics processing units (GPUs) found in most personal computers today. The development of frameworks based on the Python programming language has greatly simplified the development process. Together, this lowered the barriers to entry into the deep learning industry and ensured the popularity it has today
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