Enhancing cardiac diagnostics through semantic-driven image synthesis: a hybrid GAN approach

Cardiac image synthesis and analysis are pivotal in modern healthcare for accurate diagnosis and treatment of cardiovascular conditions. This study introduces a pioneering approach—hybrid GAN with semantic resonance—for the generation and analysis of synthetic cardiac images. Drawing from a diverse dataset encompassing local and global views, acquired through various modalities like MRI, CT, and echocardiography, the critical need for both accuracy and clinical relevance in cardiac image synthesis is addressed. In the architectural setup, a hybrid GAN is defined, comprising both local and global components. The local generator excels in capturing intricate details, while the global generator contextualizes the overall cardiac structure. A fundamental innovation lies in the incorporation of pre-trained CNN classifiers specialized in recognizing cardiac pathologies, anatomical structures, and clinical features. These classifiers provide conditional guidance to the generators, ensuring that the synthesized images align semantically with clinical expectations. The training phase employs a pioneering approach, integrating real-time feedback from the classifiers to steer the image synthesis process. The generators, discriminators, and classifiers are optimized, with a dual emphasis on adversarial loss for authenticity and classification loss for diagnostic significance. The accuracy obtained by the proposed approach is 98.96%. Further, the SSIM and PSNR values attained by the proposed approach are 0.955 and 45.23, which is higher than the existing approaches like GAN, CNN, VAE, DenseNet, VGG19, DeepCardiac, Pix2Pix GAN and Cycle-GAN, respectively. Validation on an independent test dataset underscores the generalization capabilities of the proposed hybrid GAN. Furthermore, post-processing techniques refine the generated images, elevating their clinical relevance. Results visualization presents the prowess of the proposed approach, providing a holistic view of local and global perspectives within synthetic images.