# Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation

arXiv: https://arxiv.org/abs/2112.14320 · HTML: https://mlsanity.com/paper/2112.14320/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 28 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [An overview of deep learning in medical imaging focusing on MRI](https://mlsanity.com/paper/1811.10052/) (2018)
- [Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning](https://mlsanity.com/paper/1909.00273/) (2019)
- [Vessel Segmentation and Catheter Detection in X-Ray Angiograms Using Superpixels](https://mlsanity.com/paper/1709.02741/) (2017)
- [Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images](https://mlsanity.com/paper/1809.07786/) (2018)
- [A Systematic Approach for MRI Brain Tumor Localization, and Segmentation using Deep Learning and Active Contouring](https://mlsanity.com/paper/2102.03532/) (2021)
- [Liver segmentation in CT images using three dimensional to two dimensional fully convolutional network](https://mlsanity.com/paper/1802.07800/) (2018)
- [Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple Image Scales](https://mlsanity.com/paper/2002.01975/) (2020)

## Abstract (arXiv)

Brain tumor analysis in MRI images is a significant and challenging issue because misdiagnosis can lead to death. Diagnosis and evaluation of brain tumors in the early stages increase the probability of successful treatment. However, the complexity and variety of tumors, shapes, and locations make their segmentation and classification complex. In this regard, numerous researchers have proposed brain tumor segmentation and classification methods. This paper presents an approach that simultaneously segments and classifies brain tumors in MRI images using a framework that contains MRI image enhancement and tumor region detection. Eventually, a network based on a multitask learning approach is proposed. Subjective and objective results indicate that the segmentation and classification results based on evaluation metrics are better or comparable to the state-of-the-art.