GAN-Enhanced Medical Image Synthesis: Augmenting CXR Data for Disease Diagnosis and Improving Deep Learning Performance
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Abstract
Deep learning is increasing the need for accurate and reliable medical image analysis tools, especially for CXR disease diagnosis. This study proposes the Attention Mechanisms based Cycle-Consistent GAN (AM-CGAN) to address the lack of annotated medical data. To produce realistic and clinically relevant CXR images, our model uses Generative Adversarial Networks (GAN) and attention mechanisms. Downstream deep learning models for disease classification improve with this enhancement. The Attention Mechanisms based Cycle-Consistent GAN (AM-CGAN) improves the accuracy and reliability of deep learning models used for medical image analysis, specifically for Chest X-ray (CXR) data. CXR data is enhanced to improve disease diagnosis. Generative Adversarial Networks (GAN) create realistic medical images in the proposed model. It also uses attention mechanisms to highlight key areas in generated images. This research aims to address the lack of annotated medical data, particularly for CXR images. Training deep learning models is difficult due to the lack of diverse and well-annotated datasets. Our proposed AM-CGAN uses attention mechanisms to generate synthetic CXR images that closely resemble medical images and highlight disease-specific characteristics. AM-CGAN uses Cycle-Consistent GAN to ensure that generated images match the input distribution and prevent mode collapse. While synthesizing images, the model can selectively focus on important anatomical structures and pathological indicators using attention mechanisms. This attention-driven approach improves the clinical significance of generated images, making them better for training accurate and reliable disease classification models. Many experiments were done to test the AM-CGAN on CXR images of COVID-19, pneumonia, and normal cases. The quantitative results show high precision (98.15% accuracy). This shows the model's ability to create medical-data-like synthetic images. Downstream deep learning models trained on the augmented dataset perform better at capturing disease-specific characteristics. This study advances GAN-enhanced medical image synthesis research and addresses the data shortage in medical imaging research. The AM-CGAN attention-driven focus on disease-related regions in CXR data suggests a promising way to improve diagnostic models, especially in situations with few labeled datasets. The AM-CGAN bridges the gap between diverse data and sophisticated deep learning models for disease diagnosis, making it a major advancement in medical image analysis.
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