Attention-Augmented MobileNetV2 for MRI-Based Brain Tumor Classification: Cosine Annealing and Advanced Metrics
Attention mechanisms integrated into MobileNetV2 with cosine annealing and stratified k-fold validation for glioma, meningioma, and pituitary tumor classification. ROC-AUC: 0.9703.
About the author
This paper is authored by Vanshaj Awasthi, a Full Stack Developer based in Pune, Maharashtra, India, with 17+ peer-reviewed publications across IEEE and Springer in medical AI and deep learning, and two IEEE Best Paper Awards. Bachelor of Technology - Computer Science, Amity University Mumbai.
Related publications
- Synergizing GAN-Driven Synthetic Data Pipelines with Deep Neural Networks for Enhanced Breast Cancer Diagnosis
GAN-generated synthetic histopathology images augment CNN training to achieve 99.17% accuracy, 100% recall, MCC 0.9801, and…
- Priority-Driven MAC Protocol Design for IoT-Enabled Wireless Body Area Networks
Priority-aware MAC protocol for WBANs reduces signal collisions, cuts energy consumption, and improves reliability for real-time…
- Reinforcement Learning and Spatio-Temporal GNNs for Alzheimer's Disease Progression Prediction with VAE-Based Data Imputation
Spatio-temporal GNNs + reinforcement learning + VAE-based imputation for Alzheimer's progression modeling across 2,149 ADNI MRI…