Efficiency in the classification of chest X-ray images through generative parallelization of the Neural Architecture Search

Explore GenNAS for chest X-ray classification in lung diseases, leveraging novel parallel training methods for enhanced accuracy and efficiency. Medical image classification for pulmonary pathologies from chest X-rays is traditionally time-consuming. GenNAS, using GPT-4's generative capabilities, automates optimal architecture learning from data. This study investigates parallelization and generative algorithms to optimize neural network architectures for chest X-ray classification, analyzing their impact on the NAS algorithm using the ChexPert dataset. The study uses the CheXpert dataset with 224,316 chest X-rays to classify five lung disease pathologies. GenNASXRays evaluates 6561 architecture possibilities in an 8-layer search space, with AUC-ROC and Precision-Recall plots as metrics. Training on 187,641 images, the sequential algorithm took 190.2 hours for an AUC-ROC of 0.869. In parallel execution on two GPUs, an AUC-ROC of 0.87 was achieved in 127.09 hours, highlighting the efficiency of parallelization.