Capsule Networks – A survey

Mensah, Patrick Kwabena; Adekoya, Adebayo Felix; Ayidzoe, Mighty Abra; Edward, Baagyire Y.

doi:10.1016/j.jksuci.2019.09.014

Cited by 152 publications

(86 citation statements)

References 21 publications

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“…Varying the momentum, batch size, learning rate, dropout, and learning rate decay did not significantly affect the performance of the CapsNets models as observed in [30]. The single most important hyperparameter that significantly affected the performance of the CapsNet models was the number of routing iterations with three producing the best performance values.…”

Section: A Model Flexibility and Robustnessmentioning

confidence: 74%

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Gabor Capsule Network for Plant Disease Detection

Mensah¹,

Weyori²,

Ayidzoe³

2020

IJACSA

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Crop diseases contribute significantly to food insecurity, malnutrition, and poverty in Africa where the majority of the population is into Agriculture. Manual plant disease recognition methods are widespread but limited, ineffective, costly, and time-consuming making the need to search for automatic and efficient methods of recognition more crucial. Machine learning and Convolutional Neural Networks have been applied in other jurisdictions in an attempt to solve these problems. They have achieved impressive results in this domain but tend to be 'data-hungry', invariant, and vulnerable to attacks that can easily lead to misclassifications. Capsule Networks, on the other hand, avoids the weaknesses of CNNs and has not been widely used in this area. This article, therefore, proposes the use of Gabor and Capsule network to recognize blurred, deformed, and unseen tomato and citrus disease images. Experimental results show that the proposed model can achieve a 98.13% test accuracy which is comparable to the performance of state-of-theart CNN models in the literature. Also, the proposed model outperformed two state-of-the-art deep learning models (which were implemented as baseline models) in terms of robustness, flexibility, fast converges, and having fewer parameters. This work can be extended to other crops and may well serve as a useful tool for the recognition of unseen plant diseases under bad weather and bad illumination conditions.

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Section: A Model Flexibility and Robustnessmentioning

confidence: 74%

“…To constrain the value of the CC"s output between the range [0,1], the squashing function is applied. CapsNets have performed well on a wide range of problems [30].…”

Section: Gabor Capsule Networkmentioning

confidence: 99%

Gabor Capsule Network for Plant Disease Detection

Mensah¹,

Weyori²,

Ayidzoe³

2020

IJACSA

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“…Most previous studies have concentrated on exploiting the CNN algorithm to extract features from a signal. Nonetheless, CNN loses the spatial information of a given signal during feature extraction, which leads to an inaccurate result in MC [51]. To solve this issue in conventional CNN, this study proposes a TL-CapsNet.…”

Section: Two-layer Capsule Network (Tl-capsnet)-based Amcmentioning

confidence: 99%

“…The routing logits are updated by increasing the iteration level. The formula for updating the routing logits is given in [51] as:…”

Section: Feature Extractionmentioning

confidence: 99%

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

et al. 2021

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The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.

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“…Table 3 shows the class-wise classification performance of the model; we obtained a reasonable result for the COVID-19 diagnosis using our method with 99.77% PREC, 99.67% REC, and 99.13% F-SCORE with a spatial resolution of 112×112. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 the detection of COVID-19 infection using CXR images and capsule networks [44].…”

Section: Performance Evaluationmentioning

confidence: 99%

Hybrid-COVID: A novel hybrid 2D/3D CNN based on cross-domain adaptation approach for COVID-19 screening from chest X-ray images

Bayoudh

Hamdaoui

Mtibaa

2020

Preprint

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So far, COVID-19, the novel coronavirus, continues to spread rapidly in most countries of the world, putting people's lives at risk. According to the WHO, respiratory infections occur primarily in the majority of patients treated with COVID-19. For decades, chest X-ray (CXR) technologies have proven their ability to accurately detect and treat respiratory diseases. Deep learning techniques, as well as the availability of a large number of CXR samples, have made a significant contribution to the fight against this pandemic. However, the most common screening methods are based on 2D CNNs, since 3D counterparts are enormously costly and labor-intensive. In this study, a hybrid 2D/3D convolutional neural network (CNN) architecture for COVID-19 screening using CXRs has been developed. The proposed architecture consists of the incorporation of a pre-trained deep model (VGG-16) and a shallow 3D CNN, combined with a depth-wise separable convolution layer and a spatial pyramid pooling module (SPP). Specifically, the depth-wise separable convolution helps to preserve the useful features while reducing the computational burden of the model. The SPP module is designed to extract multi-level representations from intermediate ones. Experimental results show that the proposed framework can achieve reasonable performances when evaluated on a collected dataset (3 classes: COVID-19, Pneumonia, and Normal). Notably, it achieved a sensitivity of 98.33%, a specificity of 98.68% and an overall accuracy of 96.91%

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Capsule Networks – A survey

Cited by 152 publications

References 21 publications

Gabor Capsule Network for Plant Disease Detection

Gabor Capsule Network for Plant Disease Detection

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

Hybrid-COVID: A novel hybrid 2D/3D CNN based on cross-domain adaptation approach for COVID-19 screening from chest X-ray images

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