Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation
This work addresses the critical problem of accurate brain tumor diagnosis in medical imaging, which can reduce misdiagnosis risks, but it appears incremental as it builds on existing multitask approaches.
The paper tackles brain tumor segmentation and classification in MRI images using a multitask learning framework with image enhancement and tumor detection, achieving results that are better or comparable to state-of-the-art methods based on evaluation metrics.
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.