Binary Classification of Brain Tumor using Early and Late fusion - Nandurbarkar, Shlok; Singh, Abhyuday; Bute, Himanshu; K., Nandhini; Gite, Shilpa; Rajamani, Kumar (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This research delves into the realm of medical imaging and artificial intelligence to enhance the classification of brain tumors, specifically distinguishing between Grade III and Grade IV gliomas. Leveraging the TCGA-GBM dataset, encompassing various image modalities such as Flair, T1, T1ce, T2, and Mask, acquired through magnetic resonance imaging (MRI), the study explores the efficacy of deep learning techniques. Both early and late fusion strategies are employed to amalgamate information from diverse modalities. The convolutional neural network (CNN)-based models exhibit commendable performance in accurately categorizing glioma types, showcasing promise for potential applications in clinical diagnostics.