Adaptive-CGAN: a comprehensive framework for generative modeling on google cloud platform
TL;DR
Results demonstrate that Adaptive-CGAN can improve diagnostic performance, synthetic image quality, computational efficiency, and environmental sustainability in AI-assisted healthcare.
Abstract
Machine learning has become increasingly important in medical diagnosis, yet its effectiveness depends on access to large, reliable, and high-quality datasets. During epidemics and emerging diseases, such as COVID-19, acquiring sufficient real-world medical images rapidly is challenging. To address these issues, this study presents an Adaptive Conditional Generative Adversarial Network (Adaptive-CGAN) integrated with a cloud-based medical image processing framework. The proposed approach makes three main contributions. First, Adaptive-CGAN generates high-fidelity synthetic medical images that closely resemble real samples while improving the distinction between real and fake images. Second, a scalable TensorFlow Records (TFRecords)-based pipeline is implemented on Google Cloud Platform (GCP) to support efficient storage, loading, and processing of large-scale medical datasets. Third, a real-world COVID-19 medical image dataset comprising four disease classes is compiled and used to enhance diagnostic prediction. Experimental evaluation was conducted against several baseline generative models, including AC-GAN, WGAN, Pix2Pix, BigGAN, and CWGAN, with AC-GAN serving as the primary like-for-like baseline. Adaptive-CGAN improved classification accuracy from 93.75% ± 1.10% to 99.60% ± 0.40%, increased the Inception Score from 7.20 ± 0.28 to 8.47 ± 0.18 (+ 17.65%), reduced FID from 28.41 ± 1.35 to 26.13 ± 0.82 (− 8.02%), and reduced KID from 0.0156 ± 0.0021 to 0.0143 ± 0.0015 (− 8.33%). Adaptive-CGAN also reduced training time by 15.8% and CO₂ emissions by 9.5% compared with AC-GAN. Moreover, the GCP-based Adaptive-CGAN deployment emitted only 0.0023 kg CO₂, compared with 0.0047 kg CO₂ for the local setup, representing an approximately 50% reduction due to optimized cloud execution and the TFRecord-based pipeline. These results demonstrate that Adaptive-CGAN can improve diagnostic performance, synthetic image quality, computational efficiency, and environmental sustainability in AI-assisted healthcare.
