# Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification Source: [Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:32:19.733Z Authors: Sergey Korolev, Amir Safiullin, Mikhail Belyaev, Yulia Dodonova Published: 2017-01-23T21:54:50.000Z ## Abstract In the recent years there have been a number of studies that applied deep learning algorithms to neuroimaging data. Pipelines used in those studies mostly require multiple processing steps for feature extraction, although modern advancements in deep learning for image classification can provide a powerful framework for automatic feature generation and more straightforward analysis. In this paper, we show how similar performance can be achieved skipping these feature extraction steps with the residual and plain 3D convolutional neural network architectures. We demonstrate the performance of the proposed approach for classification of Alzheimer's disease versus mild cognitive impairment and normal controls on the Alzheimer's Disease National Initiative (ADNI) dataset of 3D structural MRI brain scans. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()