CNN Parameter Adjustment for Brain Tumor Classification

Being considered as one of the most prominent and detrimental neurological disorders, diagnosing what category of brain tumor disease as soon as possible is tremendously imperative for patients, which is excessively relied on human factors on determining brain tumor type. In order to address the said issue and enhance the classifying performance in deep learning, the paper proposes a myriad of methods combined with Convolutional Neural Networks, namely transfer learning, data augmentation, the arrangement between Batch Normalization and Dropout. Eventual experimental results prove that the proposed approaches outperform the state-of-the-art papers on the benchmark brain tumor dataset. The proposed architecture for each particular Convolutional Neural Network depicts that the outcomes are more prospective than those original methods and default-set parameters. The highest accuracy conducted experiments is 98.8%.