Melanoma Recognition by Fusing Convolutional Blocks and Dynamic Routing between Capsules

Pérez, Eduardo; Ventura, Sebastián

doi:10.3390/cancers13194974

Cited by 16 publications

(9 citation statements)

References 56 publications

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“…According to [50], the dynamic routing algorithm in CapsNet may be described as an optimization problem that involves minimizing an objective function. This stabilizes the training process by preventing the activation probabilities from falling severely out of balance during iterations.…”

Section: Modification In Capsnetmentioning

confidence: 99%

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Capsule Network with Its Limitation, Modification, and Applications—A Survey

Haq

Sethi

Rehman

2023

MAKE

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Numerous advancements in various fields, including pattern recognition and image classification, have been made thanks to modern computer vision and machine learning methods. The capsule network is one of the advanced machine learning algorithms that encodes features based on their hierarchical relationships. Basically, a capsule network is a type of neural network that performs inverse graphics to represent the object in different parts and view the existing relationship between these parts, unlike CNNs, which lose most of the evidence related to spatial location and requires lots of training data. So, we present a comparative review of various capsule network architectures used in various applications. The paper’s main contribution is that it summarizes and explains the significant current published capsule network architectures with their advantages, limitations, modifications, and applications.

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Section: Modification In Capsnetmentioning

confidence: 99%

“…[18] addressed the issue. According to [50], a more general method entails rescaling the weight matrix to guarantee that the inner product between input and the weighted sum (sj) of all individual primary capsule predictions for capsule j is less than 1 for each iteration.…”

Section: Modification In Capsnetmentioning

confidence: 99%

Capsule Network with Its Limitation, Modification, and Applications—A Survey

Haq

Sethi

Rehman

2023

MAKE

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“…Other comparison models include VGG16, Res-Net18, ResNet50, ResNet101, Inception V3, and CNN + Caps. Where CNN + Caps is as shown in Figure 11, the CNN module selects two forms: the convolution module in literature [22] and the convolution module in the TongueCaps model, which removes the shortcut connection structure for the convenience of the following description. The former is recorded as CNN1 + Caps, and the latter is recorded as CNN2 + Caps.…”

Section: Experimental Comparison Modelmentioning

confidence: 99%

“…Another model framework, the CapsNet proposed by Professor Hinton [21], which not only greatly reduces the size of mode, but also makes more effective use of spatial location information, and it better encodes the relationship between local information and global goal. In some studies, it has been gradually confirmed to have better performance on classification tasks, which are of a limited number and low resolution datasets [22][23][24][25][26] compared to some CNN models, such as AlexNet [27], NDCNN [28], and NPMIL [29].…”

Section: Introductionmentioning

confidence: 99%

TongueCaps: An Improved Capsule Network Model for Multi-Classification of Tongue Color

Yan

Jiang

2022

Diagnostics

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Tongue color is an important part of tongue diagnosis. The change of tongue color is affected by pathological state of body, blood rheology, and other factors. Therefore, physicians can understand a patient’s condition by observing tongue color. Currently, most studies use machine learning, which is time consuming and labor intensive. Other studies use deep learning based on convolutional neural network (CNN), but the affine transformation of CNN is less robust and easily loses the spatial relationship between features. Recently, Capsule Networks (CapsNet) have been proposed to overcome these problems. In our work, CapsNet is used for tongue color research for the first time, and improved model TongueCaps is proposed, which combines the advantage of CapsNet and residual block structure to achieve end to end tongue color classification. We conduct experiments on 1371 tongue images; TongueCaps achieve accuracy is 0.8456, sensitivity is 0.8474, and specificity is 0.9586. In addition, the size of TongueCaps is 8.11 M, and FLOPs is 1,335,342, which are smaller than CNN in comparison models. Experiments have confirmed that the CapsNet can be used for tongue color research, and improved model TongueCaps, in this paper, is superior to other comparison models in terms of accuracy, specificity and sensitivity, computational complexity, and size of model.

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“…CapsNet proposed by Hinton [3] not only greatly reduces the size of the model but also more effectively utilizes spatial position information and better encodes the relationship between local information and global objectives. In some studies, it has been gradually proven to perform better on classification tasks with limited numbers and low-resolution datasets [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Syndrome Types Classification Method of Skin Diseases Based on Tongue Hierarchical Feature Fusion

Wang,

Wang

et al. 2023

Preprint

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Tongue diagnosis is a non-invasive, painless diagnostic method by observing the tongue image of patients to diagnose and analyze their pathological conditions, which provides an opportunity for the future development of tongue diagnosis. However, the traditional tongue diagnosis method mainly relies on the experience and judgment of doctors, and is also easily affected by external factors. These factors hinder the development and application of tongue diagnosis. Currently, most studies use machine learning, which is time consuming and labor intensive. Other studies use deep learning based on convolutional neural network (CNN), but the affine transformation of CNN is less robust and easily loses the spatial relationship between features. In this work, we propose a traditional Chinese medicine (TCM) syndrome classification model of skin diseases based on tongue image hierarchical feature fusion. By adding a multi-scale residual module to the feature extraction part of the capsule network, we can extracted richer feature of tongue image. At the same time, the attention mechanism module is embedded in the multi-scale residual module, with the help of the attention mechanism module, the interference of tongue impurity information is suppressed, and accurate features are extracted for classification. Through experiments, it has been proven that our proposed method has achieved accuracy of 89.6\% in the classification of tongue for acne syndrome, and accuracy of 91.6\% in the dermatitis syndrome.

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Melanoma Recognition by Fusing Convolutional Blocks and Dynamic Routing between Capsules

Cited by 16 publications

References 56 publications

Capsule Network with Its Limitation, Modification, and Applications—A Survey

Capsule Network with Its Limitation, Modification, and Applications—A Survey

TongueCaps: An Improved Capsule Network Model for Multi-Classification of Tongue Color

Syndrome Types Classification Method of Skin Diseases Based on Tongue Hierarchical Feature Fusion

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