LCDEiT: A Linear Complexity Data-Efficient Image Transformer for MRI Brain Tumor Classification

Current deep learning-assisted brain tumor classification models sustain inductive bias and parameter dependency problems for extracting texture-based image information. Thereby concerning these problems, the recent development of the vision transformer model has substituted the DL model for classification tasks. However, the high performance of the vision transformer model depends on a large-scale dataset as well as self-attention calculations between the number of image patches which result in a quadratic computational complexity. To address these problems, the vision transformer must be data-efficient to be well-trained with a limited amount of data, and the computational complexity must be linear with the number of image patches. Consequently, this paper presents a novel linear-complexity data-efficient image transformer called LCDEiT for training with small-size datasets by using a teacher-student strategy and linear computational complexity concerning the number of patches using an external attention mechanism. The teacher model comprised a custom gated-pooled convolutional neural network to provide knowledge to the transformer-based student model for the classification of MRI brain tumors. The average classification accuracy and F1-score for two benchmark datasets including Figshare and BraTS-21 are found 98.11% and 97.86% and 93.69% and 93.68% respectively. The results indicate that the proposed model could have a great impact on medical imaging-based diagnosis where data availability and faster computations are the main concern.