# Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/deep-learning-for-brain-tumor-segmentation-a-survey-of-state-of-the-art/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Tirivangani Magadza,Serestina Viriri |
| Citations | 228 |
| DOI | 10.3390/jimaging7020019 |
| Fields | Computer Science,Neuroscience |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.mdpi.com/2313-433X/7/2/19/pdf?version=1611930796 |
| OpenAlex ID | https://openalex.org/W3127966350 |
| PMID | 34460618 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Serestina Viriri](https://scholariq.org/researchers/serestina-viriri/)

## Paper primary topic

- [Brain Tumor Detection and Classification](https://scholariq.org/topics/brain-tumor-detection-and-classification/)

## Paper topics

- [Brain Tumor Detection and Classification](https://scholariq.org/topics/brain-tumor-detection-and-classification/)
- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)
- [Medical Image Segmentation Techniques](https://scholariq.org/topics/medical-image-segmentation-techniques/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.