Enhanced Precision in Brain Tumor Mapping through Multi-Scale Deep Learning Models

Brain tumors pose serious health risks, and making early detection essential for enhancing patient outcomes. Traditional diagnostic methods, such as MRI scans, require specialized expertise, often limiting their accessibility and accuracy. This research introduces a novel multi-scale deep learning framework for automating brain tumor identification and precise lesion mapping using MRI images. The framework employs advanced Convolutional Neural Network (CNN) architectures, including ResNet, DenseNet, and EfficientNet, and utilizes transfer learning to enhance performance, particularly in scenarios with limited data. Integrating deep learning with advanced preprocessing techniques, the system addresses challenges such as noisy images and varying tumor shapes, enabling real-time diagnostic applications. Tested on a dataset of 4,000 MRI images, the model achieved an accuracy rate of 98%, with precision and recall scores of around 0.96, highlighting its potential for reliable tumor classification and lesion mapping in clinical decision-making. Furthermore, the framework’s robustness and scalability make it well-suited for integration into healthcare systems, supporting early diagnosis and personalized treatment planning to enhance patient care.