Brain tumor segmentation by cascaded multiscale multitask learning framework based on feature aggregation

Abstract

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. Numerous researchers have proposed brain tumor segmentation and classification methods in this regard. 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. The proposed network, called Multiscale Cascaded Multitask Network, is based on a multitask learning approach containing segmentation and classification tasks. A multiscale approach and cascade approach in layers of encoder and decoder have been applied to improve segmentation accuracy in the proposed network. In addition, to increase the classification accuracy, a feature aggregation module has been introduced that integrates different levels of features to better tumor type classification. Simultaneously learning the two tasks of segmentation and classification, along with applying the mentioned approaches, has improved the results in both tasks. 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. Our proposed method has reached 96.27 and 95.88 for DCS and mean IoU, respectively, for segmentation and 97.988 accuracies for classification.