# Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review

arXiv: https://arxiv.org/abs/1712.03747 · HTML: https://mlsanity.com/paper/1712.03747/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2017.

## Built on by (most cited first, 14 of 14)

- [FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain](https://mlsanity.com/paper/2208.14360/) (2022)
- [DFENet: A Novel Dimension Fusion Edge Guided Network for Brain MRI Segmentation](https://mlsanity.com/paper/2105.07962/) (2021)
- [Multimodal and multicontrast image fusion via deep generative models](https://mlsanity.com/paper/2303.15963/) (2023)
- [Robust Brain MRI Image Classification with SIBOW-SVM](https://mlsanity.com/paper/2311.08908/) (2023)
- [MRI Images, Brain Lesions and Deep Learning](https://mlsanity.com/paper/2101.05091/) (2021)
- [A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction](https://mlsanity.com/paper/2101.10599/) (2021)
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- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [Clipped DeepControl: deep neural network two-dimensional pulse design with an amplitude constraint layer](https://mlsanity.com/paper/2201.08668/) (2022)
- [Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation](https://mlsanity.com/paper/2405.13304/) (2024)
- [Development of CNN Architectures using Transfer Learning Methods for Medical Image Classification](https://mlsanity.com/paper/2410.16711/) (2024)
- [Intuitive Axial Augmentation Using Polar-Sine-Based Piecewise Distortion for Medical Slice-Wise Segmentation](https://mlsanity.com/paper/2412.03352/) (2024)
- [Dual-domain Multi-path Self-supervised Diffusion Model for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2503.18836/) (2025)
- [Seeing What Shouldn't Be There: Counterfactual GANs for Medical Image Attribution](https://mlsanity.com/paper/2605.05283/) (2026)

## Abstract (arXiv)

In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, detection and segmentation. These methods have also been utilised in medical image analysis domain for lesion segmentation, anatomical segmentation and classification. We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing strategies available in these works. The aim of this study is three-fold. Our primary goal is to report how different CNN architectures have evolved, discuss state-of-the-art strategies, condense their results obtained using public datasets and examine their pros and cons. Second, this paper is intended to be a detailed reference of the research activity in deep CNN for brain MRI analysis. Finally, we present a perspective on the future of CNNs in which we hint some of the research directions in subsequent years.