# Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification

arXiv: https://arxiv.org/abs/1701.06643 · HTML: https://mlsanity.com/paper/1701.06643/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2017.

## Built on by (most cited first, 30 of 48)

- [VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI](https://mlsanity.com/paper/2101.05131/) (2021)
- [Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Data](https://mlsanity.com/paper/2210.00255/) (2022)
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- [Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN](https://mlsanity.com/paper/2111.12886/) (2021)
- [Longformer: Longitudinal Transformer for Alzheimer's Disease Classification with Structural MRIs](https://mlsanity.com/paper/2302.00901/) (2023)
- [Is a PET all you need? A multi-modal study for Alzheimer's disease using 3D CNNs](https://mlsanity.com/paper/2207.02094/) (2022)
- [Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses](https://mlsanity.com/paper/2509.10620/) (2025)
- [How You Split Matters: Data Leakage and Subject Characteristics Studies in Longitudinal Brain MRI Analysis](https://mlsanity.com/paper/2309.00350/) (2023)
- [Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications](https://mlsanity.com/paper/2202.08916/) (2022)
- [Benchmarking CNN on 3D Anatomical Brain MRI: Architectures, Data Augmentation and Deep Ensemble Learning](https://mlsanity.com/paper/2106.01132/) (2021)
- [Studying the Effects of Self-Attention for Medical Image Analysis](https://mlsanity.com/paper/2109.01486/) (2021)
- [Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence](https://mlsanity.com/paper/2206.03247/) (2022)
- [BlindHarmony: "Blind" Harmonization for MR Images via Flow model](https://mlsanity.com/paper/2305.10732/) (2023)
- [Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research](https://mlsanity.com/paper/2301.08525/) (2023)
- [sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI](https://mlsanity.com/paper/2302.08967/) (2023)
- [The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection](https://mlsanity.com/paper/2309.07192/) (2023)
- [ADAPT: Alzheimer Diagnosis through Adaptive Profiling Transformers](https://mlsanity.com/paper/2401.06349/) (2024)
- [Enhancing MRI-Based Classification of Alzheimer's Disease with Explainable 3D Hybrid Compact Convolutional Transformers](https://mlsanity.com/paper/2403.16175/) (2024)
- [PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans](https://mlsanity.com/paper/2403.18328/) (2024)
- [Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval](https://mlsanity.com/paper/2210.00506/) (2022)
- [Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input](https://mlsanity.com/paper/2210.01177/) (2022)
- [Domain Aware Multi-Task Pretraining of 3D Swin Transformer for T1-weighted Brain MRI](https://mlsanity.com/paper/2410.00410/) (2024)
- [Attention-Guided Autoencoder for Automated Progression Prediction of Subjective Cognitive Decline with Structural MRI](https://mlsanity.com/paper/2206.12480/) (2022)
- [Classification of Alzheimer's Disease Using the Convolutional Neural Network (CNN) with Transfer Learning and Weighted Loss](https://mlsanity.com/paper/2207.01584/) (2022)
- [XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning](https://mlsanity.com/paper/2207.13223/) (2022)
- [Predicting multiple sclerosis disease severity with multimodal deep neural networks](https://mlsanity.com/paper/2304.04062/) (2023)
- [Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning](https://mlsanity.com/paper/2305.20058/) (2023)
- [Attention-based 3D CNN with Multi-layer Features for Alzheimer's Disease Diagnosis using Brain Images](https://mlsanity.com/paper/2308.05655/) (2023)
- [Vision Transformers and Bi-LSTM for Alzheimer's Disease Diagnosis from 3D MRI](https://mlsanity.com/paper/2401.03132/) (2024)

## Abstract (arXiv)

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.