Deep LearningBased Denoising Framework for HighFidelity Brain MRI Reconstruction

This project focuses on developing a deep learning-based framework for denoising brain MRI images using Convolutional Neural Networks (CNN) and Convolutional Denoising Autoencoders (CDAE). MRI images often suffer from noise, which can hinder the detection of important features needed for accurate medical diagnosis. The framework leverages deep learning to effectively remove noise while preserving crucial structural details in the images. The system is trained on a dataset of brain MRI images with synthetic noise added to simulate real-world conditions. The model’s denoising performance is compared to traditional methods and demonstrates improved results in maintaining image quality and anatomical features. A web platform built with Flask allows users to upload noisy MRI images, which are then processed and denoised by the model. This system aims to enhance medical imaging, particularly for brain tumor detection, by providing higher-quality, clearer MRI scans for healthcare professionals.

Keywords:

Deep Learning, Brain MRI, Image Denoising, Convolutional Neural Network, Convolutional Denoising Autoencoder, MRI Reconstruction, Medical Image Processing, Image Quality Enhancement, Flask Web Framework, Brain Tumor Detection, Data Preprocessing, Healthcare Applications.