Voting Based VGG-16 for Feature Selection and Classification of The Brain Tumor Types

Abstract

Brain tumors, especially malignant ones, can be life-threatening if not detected and treated early. Identifying benign tumors, such as Meningiomas or Pituitary tumors, enables preventive measures and long-term monitoring to detect any changes in tumor size or behavior. This ensures timely intervention if needed. ML related methods for analyzing types of brain tumors have made significant advancements but brain tumour records often have a mismatch of classes, which means that some kinds of tumours are much less common than others. This may result in models that are biased and work well for the majority of the class but not so well for the minority class. Latest research techniques have focused on deep learning approaches. Identifying brain tumor types using neural networks is a complex but promising approach in medical image analysis. Combining NN with ML techniques is what the suggested model does to get to the exact type of brain tumour. In this regard, the proposed model has extracted the features from the tuned model of VGG-16 and after extracting the features from the network to further classify the stage, the model has applied ensemble voting mechanism.