Abstract
Conventionally, fine-tuning or transfer learning using a pre-trained convolutional network is adopted to design a classifier. However, when the dataset is small this can deteriorate the classifier generalization performance due to negative transfer or overfitting issues. In this paper, we suggest a flexible and high-capacity multiple kernel-based convolutional neural network (MK-CNN) to automate the pathological brain classification task. The proposed network employed different stacks of convolution with various kernels to obtain multi-scale features from the input image. The smaller kernel size provides specific information about the local features whereas the larger kernel size provides the global spatial information. Hence, the network takes into account both regional specifics and global spatial consistency thanks to this multi-scale methodology. Only the output layer is shared between each network stack. This makes it possible to specifically tweak the CNN’s weights and biases for each convolution stack and associated kernel size. The results reported on real patient data from the Harvard Whole Brain Atlas reveal that our method outperforms state-of-the-art techniques. The suggested approach may be used to help experts carry out the clinical follow-up study.
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Data Availibility Statement
The experimental data supporting this study’s findings are openly available in Harvard Whole Brain Atlas Repository: https://www.med.harvard.edu/aanlib/home.html.
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Acknowledgements
This work is supported by the Collaborative Research and Innovation Scheme under TEQIP-III, Veer Surendra Sai University of Technology, Burla.
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Dora, L., Agrawal, S., Panda, R. et al. Pathological brain classification using multiple kernel-based deep convolutional neural network. Neural Comput & Applic 36, 747–756 (2024). https://doi.org/10.1007/s00521-023-09057-z
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DOI: https://doi.org/10.1007/s00521-023-09057-z