A robust approach for multi-type classification of brain tumor using deep feature fusion

• DOI: 10.3389/fnins.2024.1288274
• Publication Date: 2024-02-19T04:29:34Z
• Authors: Wenna Chen; Xinghua Tan; Jincan Zhang; Ganqin Du; Qizhi Fu; Hongwei Jiang
• Keywords: 03 medical and health sciences; 0302 clinical medicine; brain tumor classification; deep learning; Neurosciences. Biological psychiatry. Neuropsychiatry; transfer learning; DenseNet121; ResNet101; EfficientNetB0; RC321-571; Neuroscience

Brain tumors can be classified into many different types based on their shape, texture, and location. Accurate diagnosis of brain tumor types can help doctors to develop appropriate treatment plans to save patients’ lives. Therefore, it is very crucial to improve the accuracy of this classification system for brain tumors to assist doctors in their treatment. We propose a deep feature fusion method based on convolutional neural networks to enhance the accuracy and robustness of brain tumor classification while mitigating the risk of over-fitting. Firstly, the extracted features of three pre-trained models including ResNet101, DenseNet121, and EfficientNetB0 are adjusted to ensure that the shape of extracted features for the three models is the same. Secondly, the three models are fine-tuned to extract features from brain tumor images. Thirdly, pairwise summation of the extracted features is carried out to achieve feature fusion. Finally, classification of brain tumors based on fused features is performed. The public datasets including Figshare (Dataset 1) and Kaggle (Dataset 2) are used to verify the reliability of the proposed method. Experimental results demonstrate that the fusion method of ResNet101 and DenseNet121 features achieves the best performance, which achieves classification accuracy of 99.18 and 97.24% in Figshare dataset and Kaggle dataset, respectively.