BT-Net: An end-to-end multi-task architecture for brain tumor classification, segmentation, and localization from MRI images

Abstract

Brain tumors are severe medical conditions that can prove fatal if not detected and treated early. Radiologists often use MRI and CT scan imaging to diagnose brain tumors early. However, a shortage of skilled radiologists to analyze medical images can be problematic in low-resource healthcare settings. To overcome this issue, deep learning-based automatic analysis of medical images can be an effective tool for assistive diagnosis. Conventional methods generally focus on developing specialized algorithms to address a single aspect, such as segmentation, classification, or localization of brain tumors. In this work, a novel multi-task network was proposed, modified from the conventional VGG16, along with a U-Net variant concatenation, that can simultaneously achieve segmentation, classification, and localization using the same architecture. We trained the classification branch using the Brain Tumor MRI Dataset, and the segmentation branch using a “Brain Tumor Segmentation dataset. The integration of our method’s output can aid in simultaneous classification, segmentation, and localization of four types of brain tumors in MRI scans. The proposed multi-task framework achieved 97% accuracy in classification and a dice similarity score of 0.86 for segmentation. In addition, the method shows higher computational efficiency compared to existing methods. Our method can be a promising tool for assistive diagnosis in low-resource healthcare settings where skilled radiologists are scarce.