Deep convolutional GAN and hypernet-based neural architecture search for brain tumor diagnosis detection and classification

Abstract

Brain tumors are complex and life-threatening conditions that require accurate and efficient diagnostic approaches. However, existing approaches often face limitations in precision and computational efficiency, mainly due to the heterogeneous and limited nature of medical imaging datasets. Recent advancements in deep learning, mostly Neural Architecture Search (NAS) and Generative Adversarial Networks (GANs), have show significant potential for enhancing diagnostic performance. In this study, a novel framework integrating HyperNet-based Neural Architecture Search (HN-NAS) with Deep Convolutional Generative Adversarial Networks (DCGANs) is proposed for brain tumor detection and classification. The DCGAN model is employed to generate high-quality synthetic MRI images of brain lesions, thereby developing dataset diversity and mitigating the issue of limited training data. Meanwhile, HN-NAS is utilized to efficiently recognize optimal neural network architectures for accurate tumor diagnosis. The use of a HyperNetwork allows the generation of weights for multiple candidate architectures, provocatively decreasing the computational cost of architecture search and facilitating scalable model exploration. Experimental results establish that the proposed technique developments both segmentation and classification performance while maintaining computational efficiency. The findings designate that a reliable and scalable solution for real-time clinical applications can be accomplished by combining advanced NAS methods with generative models. Overall, this study establishes that integrating data augmentation with architecture optimization can suggestively improve medical imaging diagnosis.