Authors: Ramy Farag, Parth Upadhyay, Guilhermen DeSouza

Published on: March 18, 2024

Impact Score: 7.4

Arxiv code: Arxiv:2403.11505

Summary

• What is new: The incorporation of EfficientNet with an Attention mechanism alongside a pre-processing step in the COVID-19 detection pipeline.
• Why this is important: Manual diagnosis of COVID-19 using CT scans is inefficient, especially at high patient volumes.
• What the research proposes: A deep learning model-based pipeline utilizing EfficientNet and Attention mechanisms for automated COVID-19 detection from CT scan images.
• Results: The pipeline outperforms last year’s teams on the competition’s validation set.

Technical Details

Technological frameworks used: EfficientNet with an Attention mechanism

Models used: Deep learning models

Data used: CT scan images of the lungs

Potential Impact

Healthcare industry, specifically diagnostic services and radiology departments might benefit, while traditional manual diagnosis workflows might be disrupted.

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