End-to-end deep learning architecture for multi-class brain tumor segmentation and classification from MRI images

Abstract

Brain tumors are severe medical conditions that can prove fatal if not detected andtreated early. Radiologists often use MRI and CT scan imaging to diagnose brain tumorsearly.However, a shortage of skilled radiologists to analyze medical images can beproblematic in low-resource healthcare settings. To overcome this issue, deep learning-based automatic analysis of medical images can be an effective tool for assistive di-agnosis. Conventional methods generally focus on developing specialized algorithmsto address a single aspect, such as segmentation, classification, or localization of braintumors.Inthiswork,anovelmulti-tasknetworkwasproposed,modifiedfromthecon-ventional VGG16, along with a U-Net variant concatenation, that can simultaneouslyachieve segmentation, classification, and localization using the same architecture. Wetrain the classification branch using the Brain Tumor MRI Dataset, and the segmenta-tionbranchusingaBrainTumorSegmentationdataset.Theintegrationofourmethod’soutput can aid in simultaneous classification, segmentation, and localization of fourtypesofbraintumorsinMRIscans.Theproposedmulti-taskframeworkachieved97%accuracy in classification and a dice similarity score of 0.86 for segmentation. In addi-tion,themethodshowshighercomputationalefficiencycomparedtoexistingmethods.Our method can be a promising tool for assistive diagnosis in low-resource healthcaresettingswhereskilledradiologistsarescarce.