Advancing Brain Tumor Detection in MRI: Leveraging NAFNET Denoising for Enhanced CNN Performance

Image denoising is a vital process in the realm of medical imaging and the open-source image denoising algorithms such as NAFNET (Non-Linear Activation Free Network) can be used in improving the quality of brain MRI scans. The objective is to reduce noise in MRI images using NAFNET, thus improving the ability of subsequent CNN models to detect brain tumors. A comparative analysis was conducted between NAFNET and various traditional denoising algorithms. Different evaluation metrics were employed to demonstrate NAFNET’s effectiveness in reducing image noise while preserving important details. Additionally, the impact of NAFNET-enhanced denoising on CNN-based brain tumor detection models was evaluated using various assessment metrics. The results highlight the potential of leveraging existing open source denoising algorithms like NAFNET to improve the detailing, accuracy in scan images and enhance the overall quality of CNN models, particularly in detecting brain tumors from MRI scans.