A Reinforcement Learning-Based Adaptive Learning Rate Scheduler for Optimizing Brain Tumor Detection

One key factor that determines a properly trained deep learning model with the capability to determine medical images for brain tumor is the optimal learning rate. Traditional approaches usually are bounded with static or pre-defined adaptive learning rate schedules, thus failing to capture such problems in dynamic medical images data and not sufficient for getting the optimal performance level in the model. In this regard, we propose an innovative adaptive learning rate scheduler, which uses the concepts of reinforcement learning to adjust the learning rate during neural network training, specifically with a view to optimize the performance in the task of detection of brain tumors from MRI scans. In our proposal, the training process interacts continuously with the reinforcement learning agent to update the learning rate dynamically. It thus allows for intelligent adaptation toward the specific needs of a dataset, thus achieving quicker and more stable convergence. Implemented in a CNN framework and tested on standard brain MRI datasets, our scheduler thus demonstrates superior performance compared with traditional learning rate techniques by achieving higher accuracy and efficiency. This is the first proposed RL-based adaptive learning rate scheduler, one of the significant advancements applied to machine learning in the field of medical diagnostics so that model reliability and accuracy to detect brain tumors, as crucial for improving patient results.