Segmentation of Lungs COVID Infected Regions by Attention Mechanism and Synthetic Data

TL;DR

This paper presents a deep learning approach combining attention mechanisms and synthetic data generation to improve segmentation of COVID-19 infected lung regions in CT images, aiding diagnosis and treatment.

Contribution

It introduces a novel segmentation method using attention-enhanced CNNs and GAN-based data augmentation for better accuracy on limited datasets.

Findings

Attention blocks improve segmentation accuracy.

Synthetic data enhances model robustness.

Proposed method outperforms existing procedures.

Abstract

Coronavirus has caused hundreds of thousands of deaths. Fatalities could decrease if every patient could get suitable treatment by the healthcare system. Machine learning, especially computer vision methods based on deep learning, can help healthcare professionals diagnose and treat COVID-19 infected cases more efficiently. Hence, infected patients can get better service from the healthcare system and decrease the number of deaths caused by the coronavirus. This research proposes a method for segmenting infected lung regions in a CT image. For this purpose, a convolutional neural network with an attention mechanism is used to detect infected areas with complex patterns. Attention blocks improve the segmentation accuracy by focusing on informative parts of the image. Furthermore, a generative adversarial network generates synthetic images for data augmentation and expansion of small available datasets. Experimental results show the superiority of the proposed method compared to some existing procedures.