MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification

TL;DR

This paper introduces MAG-Net, a multi-task attention-guided network that simultaneously segments and classifies brain tumors in MRI images, improving accuracy and efficiency over existing models.

Contribution

The paper presents a novel multi-task deep learning model that combines segmentation and classification for brain tumors, with fewer training parameters and improved performance.

Findings

Achieved promising segmentation and classification accuracy.

Outperformed existing state-of-the-art models.

Used less training parameters than comparable models.

Abstract

Brain tumor is the most common and deadliest disease that can be found in all age groups. Generally, MRI modality is adopted for identifying and diagnosing tumors by the radiologists. The correct identification of tumor regions and its type can aid to diagnose tumors with the followup treatment plans. However, for any radiologist analysing such scans is a complex and time-consuming task. Motivated by the deep learning based computer-aided-diagnosis systems, this paper proposes multi-task attention guided encoder-decoder network (MAG-Net) to classify and segment the brain tumor regions using MRI images. The MAG-Net is trained and evaluated on the Figshare dataset that includes coronal, axial, and sagittal views with 3 types of tumors meningioma, glioma, and pituitary tumor. With exhaustive experimental trials the model achieved promising results as compared to existing state-of-the-art models, while having least number of training parameters among other state-of-the-art models.