Interpreting Convolutional Neural Networks for Brain Tumor Classification: An Explainable Artificial Intelligence Approach-Reference-Cited by-同舟云学术

Interpreting Convolutional Neural Networks for Brain Tumor Classification: An Explainable Artificial Intelligence Approach

Published:2023 Issue: Volume: Page:77-91
ISSN:0302-9743
Container-title:Intelligent Systems
language:
Short-container-title:

Author:

Schiavon Dieine Estela Bernieri^ORCID,Becker Carla Diniz Lopes^ORCID,Botelho Viviane Rodrigues^ORCID,Pianoski Thatiane Alves^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-45389-2_6

Reference19 articles.

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2. Brain and central nervous system cancer-IARC. (n.d.).https://www.iarc.who.int/cancer-type/brain-and-central-nervous-system-cancer/. Accessed 16 May 2023

3. Magadza, T., Viriri, S.: Deep learning for brain tumor segmentation: a survey of state-of-the-art. J. Imaging 7(2), 19 (2021). https://doi.org/10.3390/jimaging7020019

4. Fernando, K.R.M., Tsokos, C.P.: Deep and statistical learning in biomedical imaging: state of the art in 3D MRI brain tumor segmentation. Inf. Fusion 92, 450–465 (2023). https://doi.org/10.1016/j.inffus.2022.12.013

5. DSA, E.: Chapter 43 - Pooling layers in convolutional neural networks. In: Deep Learning Book, 10 December 2022. https://www.deeplearningbook.com.br/camadas-de-pooling-em-redes-neurais-convolucionais

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1. Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach;Frontiers in Computational Neuroscience;2024-06-12

2. NeuroNet19: an explainable deep neural network model for the classification of brain tumors using magnetic resonance imaging data;Scientific Reports;2024-01-17