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

Overview

1. Department of Artificial Intelligence and Data Science, Panimalar Engineering College, Chennai, India
2. Department of Information Technology, Coimbatore Institute of Technology, Coimbatore, India

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.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/masoudnickparvar, at Kaggle; found in the references

Data Availability

All relevant data are within the manuscript.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 9 MeSH terms, 28 references.

Cite

This paper

Swathi, S., & Rajalakshmi, M. (2026). Deep convolutional GAN and hypernet-based neural architecture search for brain tumor diagnosis detection and classification. PloS one, 21(7), e0352353. https://doi.org/10.1371/journal.pone.0352353

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