Abstract
Brain tumor recognition is a challenging task, and accurate diagnosis increases the chance of patient survival. In this article, we propose a two-channel deep neural network architecture for tumor classification that is more generalizable. Initially, local feature representations are extracted from convolution blocks of InceptionResNetV2 and Xception networks and are vectorized using proposed pooling-based techniques. An attention mechanism is proposed that allows more focus on tumor regions and less focus on non-tumor regions which eventually helps to differentiate the type of tumor present in the images. The proposed two-channel model allows joint training of two sets of tumor image representations in an end-to-end manner to achieve good generalization. Empirical studies on Figshare and BraT’S2018, benchmark datasets, reveal that our approach is superior in terms of generalization and simple in terms of number of layers compared to the existing complex models that follow fine-tuning of deep CNN models. Avoiding too much preprocessing and augmentation techniques, the proposed model sets new state-of-the-art scores on both the brain tumor datasets.
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Bodapati, J.D., Shaik, N.S., Naralasetti, V. et al. Joint training of two-channel deep neural network for brain tumor classification. SIViP 15, 753–760 (2021). https://doi.org/10.1007/s11760-020-01793-2
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DOI: https://doi.org/10.1007/s11760-020-01793-2