A robust MRI-based brain tumor classification via a hybrid deep learning technique

Abstract The brain is the most vital component of neurological system. Therefore, tumor classification a very challenging task in field medical image analysis. There has been qualitative leap artificial intelligence, deep learning, and their imaging applications last decade. importance this remarkable development emerged biomedical engineering due to sensitivity seriousness issues related it. use learning detecting classifying tumors general particular using magnetic resonance (MRI) crucial factor accuracy speed diagnosis. This its great ability deal with huge amounts data avoid errors resulting from human intervention. aim research develop an efficient automated approach for assist radiologists instead consuming time looking at several images precise proposed based on 3064 T1-weighted contrast-enhanced MR (T1W-CE MRI) 233 patients. In study, system results five different models combined potential multiple models, trying achieve promising results. led significant improvement results, overall 99.31%.