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.
