A Novel Approach to Classify Brain Tumor with an Effective Transfer Learning based Deep Learning Model

Abstract

Abstract World's deadliest disease is brain tumor. Misdiagnosed cancers and inadequate treatment reduce survival. However, magnetic resonance imaging (MRI) is utilized for tumor analysis, but the enormous number of pictures produced by MRI makes it time-consuming and difficult to diagnose a patient just because of its complex nature, putting their life at risk. Thus, accurately detecting early brain cancers manually is difficult. We need an autonomous, intelligent system to detect brain cancers early and accurately. This study proposes a pre-trained EfficientNetb4 model with an adjusttable learning rate and custom callback to efficiently classify tumors. The proposed methodology improves the quality and quantity of the publicly accessible Br35h dataset by applying data augmentation techniques. The suggested model had 99.67% accuracy on Br35h data and 0.33% miss categorization. This method, however, had 99.87% accuracy on supplemented data and 0.13% miss categorization. The system achieves 99.33%, 99.97%, 99.93%, 99.33%, 0.66%, and 99.66% for Br35h in sensitivity, specificity, precision, NPV, FOR, and F1-score. The suggested model had 99.97% sensitivity, 99.74% specificity, 99.74% accuracy, 100% NPV, 0.66% FOR, and 99.66% F1-score for the Br35h-augmented dataset. Further, the proposed model also achieved FNR and FPR of 0 and 0.26%, respectively, and the augmented Br35H dataset achieved FNR and FPR of 0.66% and 0%, respectively. The 5Flod cross-validation also found that the model achieved an overall validation accuracy of 98.606% with a loss of 0.224%. Regarding results, the proposed system shows superior performance to other state-of-the art approaches.

Keywords

• Deep learning
• Transfer learning
• Callbacks
• Learning rate
• Medical image analysis
• Br35h
• EfficientNetB4