Abstract:
Medical imaging plays a pivotal role in modern healthcare by enabling early disease detection and
diagnosis. However, traditional deep learning models often face challenges in handling large-scale
medical image analysis due to their inability to capture long-range dependencies effectively. This
research proposes a cloud-based medical imaging analysis framework utilizing the Swin
Transformer model, which leverages a shifted window attention mechanism for improved feature
extraction. To optimize model convergence and generalization, One-Cycle Learning Rate
Optimization is integrated into the training pipeline, dynamically adjusting the learning rate to
accelerate learning and stabilize training. Existing deep learning models, such as CNNs and RNNs,
struggle with capturing long-range dependencies and hierarchical feature representations, leading
to suboptimal classification in medical imaging. Traditional static learning rate strategies often
result in slow convergence, overfitting, or poor generalization. Computational inefficiency limits
scalability, making real-time medical analysis challenging, especially for large datasets.
Additionally, cloud-based implementations of existing models lack adaptive optimization
techniques, leading to increased inference time and resource consumption. Existing deep learning
models, such as CNNs and RNNs, struggle with capturing long-range dependencies and
hierarchical feature representations, leading to suboptimal classification in medical imaging.
Traditional static learning rate strategies often result in slow convergence, overfitting, or poor
generalization. Computational inefficiency limits scalability, making real-time medical analysis
challenging, especially for large datasets. Additionally, cloud-based implementations of existing
models lack adaptive optimization techniques, leading to increased inference time and resource
consumption. |
