# Semantic Segmentation of Brain Tumors: A Performance Evaluation Using DeepLabV3+, UNet, and Intel’s OpenVINO Toolkit

**Authors:** Kapo, Akagić, Buza
**Year:** 2024
**DOI:** 10.1109/CoDIT62066.2024.10708196
**URL:** https://doi.org/10.1109/CoDIT62066.2024.10708196

## Abstract

Artificial intelligence, Machine Learning, and Deep Learning are increasingly making significant contributions to the field of medicine. Individual patient conditions, disease localization, and various influencing factors underscore the complexity of disease diagnosis and treatment planning. Introducing new technologies can revolutionize medical diagnostics, facilitating swift and accurate assessments. Among the noninvasive diagnostic methods, Magnetic Resonance Imaging (MRI) stands out, particularly in tumor diagnosis. UNet, renowned for its effectiveness in medical image analysis, serves as a robust model for semantic segmentation, as does DeepLabV3+. However, these models are inherently complex, and their inference process can be time-consuming. By leveraging the OpenVINO toolkit, the inference process is significantly reduced. In this study, nearly a 2-fold acceleration is achieved in inference time with the DeepLabV3+ model and a roughly 1.2-fold improvement with the UNet model on CPU. Moreover, when employing GPU with FP16 precision, the acceleration reached almost 2.5fold for UNet and nearly 3-fold for DeepLabV3+, showcasing the substantial performance enhancements attainable through optimized hardware utilization.

## Citation Details

Semantic Segmentation of Brain Tumors: A Performance Evaluation Using DeepLabV3+, UNet, and Intel’s OpenVINO Toolkit
**Authors**: Kapo, Akagić, Buza
**Year**: 2024
**DOI**: 10.1109/CoDIT62066.2024.10708196
**URL**: https://doi.org/10.1109/CoDIT62066.2024.10708196

**Abstract**: Artificial intelligence, Machine Learning, and Deep Learning are increasingly making significant contributions to the field of medicine. Individual patient conditions, disease localization, and various influencing factors underscore the complexity of disease diagnosis and treatment planning. Introducing new technologies can revolutionize medical diagnostics, facilitating swift and accurate assessments. Among the noninvasive diagnostic methods, Magnetic Resonance Imaging (MRI) stands out, particularly in tumor diagnosis. UNet, renowned for its effectiveness in medical image analysis, serves as a robust model for semantic segmentation, as does DeepLabV3+. However, these models are inherently complex, and their inference process can be time-consuming. By leveraging the OpenVINO toolkit, the inference process is significantly reduced. In this study, nearly a 2-fold acceleration is achieved in inference time with the DeepLabV3+ model and a roughly 1.2-fold improvement with the UNet model on CPU. Moreover, when employing GPU with FP16 precision, the acceleration reached almost 2.5fold for UNet and nearly 3-fold for DeepLabV3+, showcasing the substantial performance enhancements attainable through optimized hardware utilization.  # Truncate if very long

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*Extracted from citation research database - all 43 citations available*
