Parallel Capsule Networks for Classification of White Blood Cells
Capsule Networks (CapsNets) is a machine learning architecture proposed to overcome some of the shortcomings of convolutional neural networks (CNNs). However, CapsNets have mainly outperformed CNNs in datasets where images are small and/or the objects to identify have minimal background noise. In this work, we present a new architecture, parallel CapsNets, which exploits the concept of branching the network to isolate certain capsules, allowing each branch to identify different entities. We applied our concept to the two current types of CapsNet architectures, studying the performance for networks with different layers of capsules. We tested our design in a public, highly unbalanced dataset of acute myeloid leukaemia images (15 classes). Our experiments showed that conventional CapsNets show similar performance than our baseline CNN (ResNeXt-50) but depict instability problems. In contrast, parallel CapsNets can outperform ResNeXt-50, is more stable, and shows better rotational invariance than both, conventional CapsNets and ResNeXt-50.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationSimilar Papers 제목 키워드 기반
White blood cell subtype detection and classification
Machine learning has endless applications in the health care industry. White blood cell classification is one of the interesting and promising area of research. The classification of the white blood cells plays an import…
ClassificationMedical Diagnosisobject-detectionObject DetectionWhite blood cell classification
This paper proposes a novel automatic classification framework for the recognition of five types of white blood cells. Segmenting complete white blood cells from blood smears images and extracting advantageous features f…
Classificationfeature selectionGeneral ClassificationSegmentationW-Net: A CNN-based Architecture for White Blood Cells Image Classification
Computer-aided methods for analyzing white blood cells (WBC) have become widely popular due to the complexity of the manual process. Recent works have shown highly accurate segmentation and detection of white blood cells…
ClassificationGeneral Classificationimage-classificationImage ClassificationImproved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network
White blood cells (leukocytes) are a very important component of the blood that forms the immune system, which is responsible for fighting foreign elements. The five types of white blood cells include neutrophils, eosino…
Data AugmentationFeature EngineeringGenerative Adversarial NetworkAutomatic Classification of White Blood Cell Images using Convolutional Neural Network
Human immune system contains white blood cells (WBC) that are good indicator of many diseases like bacterial infections, AIDS, cancer, spleen, etc. White blood cells have been sub classified into four types: monocytes, l…