Parallel Capsule Networks for Classification of White Blood Cells

نویسندگان

چکیده

Capsule Networks (CapsNets) is a machine learning architecture proposed to overcome some of the shortcomings convolutional neural networks (CNNs). However, CapsNets have mainly outperformed CNNs in datasets where images are small and/or objects identify minimal background noise. In this work, we present new architecture, parallel CapsNets, which exploits concept branching network isolate certain capsules, allowing each branch different entities. We applied our two current types CapsNet architectures, studying performance for with layers capsules. tested design public, highly unbalanced dataset acute myeloid leukaemia (15 classes). Our experiments showed that conventional show similar than baseline CNN (ResNeXt-50) but depict instability problems. contrast, can outperform ResNeXt-50, more stable, and shows better rotational invariance both, ResNeXt-50.

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2021

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-030-87234-2_70