SPBTGNS: Design of an Efficient Model for Survival Prediction in Brain Tumour Patients Using Generative Adversarial Network With Neural Architectural Search Operations

The landscape of medical imaging, particularly in brain tumor analysis and survival prediction, necessitates advancements due to the inherent complexities and life-threatening nature of brain tumors. Existing methodologies often struggle with precision and efficiency, predominantly due to limitations in handling the diverse and intricate nature of medical image datasets. This research presents a novel approach that aims to improve the accuracy of survival prediction in patients with brain tumours, leveraging a Generative Adversarial Network (GAN) integrated with Neural Architectural Search (NAS) operations. The model employs the Adaptive Computation Time (ACT) Transformer, a method crucial for dynamically adjusting the number of transformer layers based on the complexity of the input sets. This feature is particularly beneficial in medical imaging for adapting to the varying complexities of brain tumor data samples. The integration of Squeeze-and-Excitation Networks (SENet) enables the model to recalibrate features channel-wise, significantly enhancing sensitivity to pivotal features in MRI images. Furthermore, the application of Google's AutoML Vision Edge offers efficient neural architecture and hyperparameter optimization, specifically tuned for image data samples. Efficient Neural Architecture Search (ENAS) is utilized to discover high-performance models with lower computational demands and the model incorporates customized loss functions, particularly Weighted Cross-Entropy Loss, addressing the class imbalance in medical datasets by emphasizing rarer tumor types. Spatial Dropout and Batch Normalization are integrated as regularization techniques to improve generalization and reduce overfitting risks. The model's efficacy was validated on the Br35H, Kaggle Brain Tumor Dataset, and IEEE Data Port Dataset Databases, exhibiting a notable improvement over existing methods: 5.9% better precision, 6.5% higher accuracy, and 4.9% higher recall in brain tumor analysis. In survival analysis, the model demonstrated 8.5% better precision and 8.3% higher accuracy, among other improvements. These enhancements underscore the model's capability in providing more accurate, efficient, and reliable predictions for brain tumor patients, potentially revolutionizing the approach to brain tumor diagnosis and survival prognostication in clinical settings.