Deep Learning based Multimodal Brain MRI Tumor Classification as a Diagnostic Tool to Benefit Clinical Applications
Abstract
Brain cancer is one of the most fatal types of disease, which is caused by an abnormally growing mass of defective brain tissue. Generally, brain cancer can be divided into benign and malignant, however, based on the World Health Organization, it can also be divided into grade I, II, III, and IV tumors. Magnetic Resonance Imaging (MRI) has become a crucial tool in the diagnosis and treatment of brain tumors. However, accurately classifying brain tumor images from MRI scans remains a challenging task due to the complexity and heterogeneity of tumor characteristics. This paper presents a deep learning based classification method for brain tumor classification .The model uses DenseNet101 and EfiiicentNetB3 and achieved 90 percent accuracy using dataset from the kaggle..
Keywords:
Glioma, Brain Tumors, Classification, EfficientNet, MRI
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