Exploring the Application of Attention Mechanisms in Conjunction with Baseline Models on the COVID-19-CT Dataset
- Chia-Chun Hsu (d4nh5u)
- Department of BioMedical Engineering (BME)
- National Cheng Kung University (NCKU)
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- Chao-Chun Cheng
- Department of Computer Science and Information Engineering (CSIE)
- National Cheng Kung University (NCKU)
This project investigates the efficacy of attention mechanisms integrated with baseline models for analyzing COVID-19-CT dataset. Our team explored different attention mechanisms like SE, CBAM, ECA, and compared their impact on baseline models such as ResNet-50, ResNeXt-50, DenseNet121, DenseNet169 and ConvNeXt-Tiny. The study involved rigorous evaluation through metrics like accuracy, precision, recall, F1-score, and the use of Grad-CAM for visualization. Our findings contribute valuable insights into the application of attention mechanisms in medical image analysis, specifically for COVID-19-CT datasets.
📣 Experimental results and in-depth analysis will be presented in a formal paper in the future. 📣
This project makes use of the COVID-19-CT dataset, generously made available by UCSD-AI4H on GitHub. We extend our sincere gratitude to UCSD-AI4H for their hard work and contribution to the open-source community. 🙇🙇
This project is licensed under the MIT License - see the LICENSE file for details.
Copyright (c) 2024 d4nh5u (Chia-Chun Hsu)