Deep learning framework for enhanced MRI analysis in healthcare diagnosis

Artificial intelligence for computer vision has improved a lot due to deep learning, but making it work better is still a big task. This is particularly important for demanding areas like diagnostic imaging that need high levels of precision and effectiveness. This study aims to improve deep learning methods to make them better for use in healthcare diagnosis, especially in analyzing MRIs. Traditional methods like Least Squares Regression, Random Forests, CNN, and Support Vector Machine can have difficulty managing big datasets, adapting to different situations, and being efficient in their calculations. In order to overcome these constraints, the suggested system adheres to a systematic procedure: In collecting data: We use a detailed MRI dataset like the BraTS (Brain Tumor Segmentation) dataset to gather a variety of labeled medical pictures for effective training. In initial processing: denoising methods are used to improve the appearance of MRI images and are standardized to a uniform scale; In augmenting data: basic augmentation methods like flipping, rotating, and increasing the intensity are used to increase the volume of data and the system’s generalizability; In training the model with transfer learning: The EfficientNet-B4 system is used over the MRI dataset. This model’s structure is effective and can easily scale, allowing it to pick up important traits for diagnostic imaging. In optimizing the model’s performance: Bayesian optimization method is used to tweak hyperparameters, making sure the model is configured optimally to maximize precision while reducing the consumption of resources. Factors such as accuracy, precision, recall, computational efficiency, and robustness are used to thoroughly assess the framework. Using these new methods, our system successfully tackles problems in medical imaging and improves DL in computer vision technology. This leads to better and more efficient tools for diagnosing health issues.