Brain MRI image segmentation using Stacked Denoising Autoencoders

Although deep learning has shown some significant achievements in image analysis and classification, their application to medical images has only recently started gaining momentum. This is because medical images are intrinsically noisier and prone to artifacts. Despite these challenges, these techniques have been shown to provide more accurate diagnoses than human doctors in certain scenarios. A major hurdle that has to be overcome in the effective classification of medical images in diagnosis is preprocessing and cleaning. This task is a major bottleneck as it requires a significant amount of time to prepare images for training, is computationally very demanding, and requires expertise about the domain.