Ensemble Deep Learning Approach for Brain Tumor Classification Using Vision Transformer and Convolutional Neural Network

The treatment plan for brain tumors varies depending on the type and stage of the tumor. Early diagnosis plays a vital role in determining appropriate treatment. In addition to clinical routines, artificial intelligence-based systems that produce automated, quantitative, and objective results can assist clinicians and scientists in making early diagnoses. For this motivation, this study proposes a deep learning-based system that classifies brain tumors obtained by magnetic resonance imaging. In the proposed approach, several wavelet transform approaches are applied to the raw dataset images. Thus, in addition to automated feature extraction in deep learning, it aimed to detect more detailed features. Therefore, four types of datasets have been obtained. Then, using the transfer learning approach, some popular convolutional neural network and vision transformer models are trained separately with the four-type datasets, and the test results are compared. The networks that produced the highest results are used to make the final decision with the ensemble technique. In the first analysis, the best performance was obtained using original data with an 83.50% accuracy value, and the second highest performance is obtained 81.72% accuracy value using the Daubhecies wavelet before deep learning. The third and fourth high performances are 81.47% and 81.22% accuracy, respectively, using original data. In the ensemble analysis, the highest result is achieved at 85.03% accuracy value using the bagging-ensemble approach of the networks, namely MobileNet-v3, vision transformer, ResNeXt, and DenseNet-201. This study demonstrates that using a hybrid wavelet transform and deep learning approach improves classification performance. This may inspire the use of the same method to solve different classification problems.