Convolutional neural networks (CNNs) for automatic classification and medical image diagnosis have recently displayed a remarkable performance. However, the CNNs fail to recognize original images rotated and oriented differently, limiting... more
Convolutional neural networks (CNNs) for automatic classification and medical image diagnosis have recently displayed a remarkable performance. However, the CNNs fail to recognize original images rotated and oriented differently, limiting their performance. This paper presents a new capsule network (CapsNet) based framework known as the multi-lane atrous feature fusion capsule network (MLAF-CapsNet) for brain tumor type classification. The MLAF-CapsNet consists of atrous and CLAHE, where the atrous increases receptive fields and maintains spatial representation, whereas the CLAHE is used as a base layer that uses an improved adaptive histogram equalization (AHE) to enhance the input images. The proposed method is evaluated using whole-brain tumor and segmented tumor datasets. The efficiency performance of the two datasets is explored and compared. The experimental results of the MLAF-CapsNet show better accuracies (93.40% and 96.60%) and precisions (94.21% and 96.55%) in feature extraction based on the original images from the two datasets than the traditional CapsNet (78.93% and 97.30%). Based on the two datasets’ augmentation, the proposed method achieved the best accuracy (98.48% and 98.82%) and precisions (98.88% and 98.58%) in extracting features compared to the traditional CapsNet. Our results indicate that the proposed method can successfully improve brain tumor classification problems and support radiologists in medical diagnostics.
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This paper proposes a new dual horizontal squash capsule network (DHS‐CapsNet) to classify the lung and colon cancers on histopathological images. DHS‐CapsNet is made up of encoder feature fusion (EFF) and a novel horizontal squash... more
This paper proposes a new dual horizontal squash capsule network (DHS‐CapsNet) to classify the lung and colon cancers on histopathological images. DHS‐CapsNet is made up of encoder feature fusion (EFF) and a novel horizontal squash (HSquash) function. The EFF aggregates the extracted feature from the 2‐lane convolutional layers, which provides rich information for better accuracy. HSquash is proposed as a squash function to ensure that vectors are effectively squashed and produces sparsity for a high discriminative capsule to extract important information from images with varied backgrounds. To present the effectiveness of DHS‐CapsNet empirically, we applied this method on histopathological images (LC25000 dataset). We achieved better results of 99.23% compared to traditional CapsNet (85.55%). The DHS‐CapsNet provides the top‐1 classification error of 0.77% compared to 14.45% of the traditional CapsNet. Our results illustrate that our method improves CapsNet and can be adopted as a computer‐aided diagnostic method to support doctors in lung and colon cancer diagnostics.
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Brain tumor recently is considered among the deadliest cancers according to research statistics and have several categories, based on the different characteristics of the tumor. Early detection of the tumor types help to devise treatment... more
Brain tumor recently is considered among the deadliest cancers according to research statistics and have several categories, based on the different characteristics of the tumor. Early detection of the tumor types help to devise treatment plans and achieve high survival rate. Human inspection is noted to be cost effective, error prone and time-consuming, which have led the interest in Convolutional Neural Networks (CNNs) to automatize the problem. However, CNNs fail to consider the precise location of the features as beneficial, which is harmful, because tumor location and its relationship with the surrounding tissue provide high influence on the brain tumor type. In addition, the CNNs require large amount of dataset for accurate training and prediction. CNNs increasingly reduce image resolution, which result to decrease in classification accuracy. In this work, we incorporated recently developed Capsule Networks (CapsNets) which overcome these drawbacks. The focused contribution is to enhance CapsNets with dilation to maintain the image resolution and improve classification accuracy. We proposed a less trainable CapsNet architecture for brain tumor classification, which takes the segmented tumor regions as inputs within the structure and has the capability of ensuring an increase focus of the CapsNets. While the baseline CapsNets consist of single convolutional layer, our proposed model introduced multiple convolutional layers which achieved an improved performance of 95.54% compared to the related works. Our results indicate that the proposed approach can improve brain tumor classification problem.
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This article is concerned with the exponential synchronization of coupled memristive neural networks (CMNNs) with multiple mismatched parameters and topology-based probability impulsive mechanism (TPIM) on time scales. To begin with, a... more
This article is concerned with the exponential synchronization of coupled memristive neural networks (CMNNs) with multiple mismatched parameters and topology-based probability impulsive mechanism (TPIM) on time scales. To begin with, a novel model is designed by taking into account three types of mismatched parameters, including: 1) mismatched dimensions; 2) mismatched connection weights; and 3) mismatched time-varying delays. Then, the method of auxiliary-state variables is adopted to deal with the novel model, which implies that the presented novel model can not only use any isolated system (regard as a node) in the coupled system to synchronize the states of CMNNs but also can use an external node, that is, not affiliated to the coupled system to synchronize the states of CMNNs. Moreover, the TPIM is first proposed to efficiently schedule information transmission over the network, possibly subject to a series of nonideal factors. The novel control protocol is more robust against these nonideal factors than the traditional impulsive control mechanism. By means of the Lyapunov-Krasovskii functional, robust analysis approach, and some inequality processing techniques, exponential synchronization conditions unifying the continuous-time and discrete-time systems are derived on the framework of time scales. Finally, a numerical example is provided to illustrate the effectiveness of the main results.
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The squash function in capsule networks (CapsNets) dynamic routing is less capable of performing discrimination of non-informative capsules which leads to abnormal activation value distribution of capsules. In this paper, we propose... more
The squash function in capsule networks (CapsNets) dynamic routing is less capable of performing discrimination of non-informative capsules which leads to abnormal activation value distribution of capsules. In this paper, we propose vertical squash (VSquash) to improve the original squash by preventing the activation values of capsules in the primary capsule layer to shrink non-informative capsules, promote discriminative capsules and avoid high information sensitivity. Furthermore, a new neural network, (i) skip-connected convolutional capsule (S-CCCapsule), (ii) Integrated skip-connected convolutional capsules (ISCC) and (iii) Ensemble skip-connected convolutional capsules (ESCC) based on CapsNets are presented where the VSquash is applied in the dynamic routing. In order to achieve uniform distribution of coupling coefficient of probabilities between capsules, we use the Sigmoid function rather than Softmax function. Experiments on Guangzhou Women and Children’s Medical Center (GWCMC), Radiological Society of North America (RSNA) and Mendeley CXR Pneumonia datasets were performed to validate the effectiveness of our proposed methods. We found that our proposed methods produce better accuracy compared to other methods based on model evaluation metrics such as confusion matrix, sensitivity, specificity and Area under the curve (AUC). Our method for pneumonia detection performs better than practicing radiologists. It minimizes human error and reduces diagnosis time.
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Research Interests:
Availability of massive amounts of data is a key contributing factor that influences the performance of deep learning models. Convolutional Neural Networks for instance, require large amounts of data in different variations to enable them... more
Availability of massive amounts of data is a key contributing factor that influences the performance of deep learning models. Convolutional Neural Networks for instance, require large amounts of data in different variations to enable them generalize well to viewpoints. However, in health and other application domains, data generation and processing tasks are time-consuming and requires annotation by experts. Capsule Network (CapsNet) have been proposed to curtail the limitations of Convolutional Neural Networks (CNNs). Due to the problem of crowding, capsule Networks perform badly on complex and real-life images such as CIFAR 10 and some medical images. In this study, a variant of a capsule network with a new algorithm referred to as the amplifier and a new squash function termed exponential squash is proposed. The amplifier is implemented in the encoder network to improve the texture of the images and has the ability to assign low relevance to irrelevant features and high relevance to vital features. The exponential squash function reduces the coupling strength of unrelated capsules in the lower and upper capsule layer. The proposed algorithm was evaluated on four datasets; CIFAR 10, fashion-MNIST, eye disease dataset and ODIR dataset achieving accuracies of 84.56% 93.76%, 89.02% and 87.27% respectively. This work sheds light on the possibility of applying CapsNet on complex real-world tasks. The proposed model can serve as an intelligent tool to aid medical personnel to diagnose eye disease and apply the necessary treatments.
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Artificial Intelligence (AI) has been evident in the agricultural sector recently. The objective of AI in agriculture is to control crop pests/diseases, reduce cost, and improve crop yield. In developing countries, the agriculture sector... more
Artificial Intelligence (AI) has been evident in the agricultural sector recently. The objective of AI in agriculture is to control crop pests/diseases, reduce cost, and improve crop yield. In developing countries, the agriculture sector faces numerous challenges in the form of knowledge gap between farmers and technology, disease and pest infestation, lack of storage facilities, among others. In order to resolve some of these challenges, this paper presents crop pests/disease datasets sourced from local farms in Ghana. The dataset is presented in two folds; the raw images which consists of 24,881 images (6,549-Cashew, 7,508-Cassava, 5,389-Maize, and 5,435-Tomato) and augmented images which is further split into train and test sets. The latter consists of 102,976 images (25,811-Cashew, 26,330-Cassava, 23,657-Maize, and 27,178-Tomato), categorized into 22 classes. All images are de-identified, validated by expert plant virologists, and freely available for use by the research community.
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Research Interests:
Research Interests:
Capsule Networks have shown great promise in image recognition due to their ability to recognize the pose, texture, and deformation of objects and object parts. However, the majority of the existing capsule networks are deterministic with... more
Capsule Networks have shown great promise in image recognition due to their ability to recognize the pose, texture, and deformation of objects and object parts. However, the majority of the existing capsule networks are deterministic with limited ability to express uncertainty. Many of them tend to be overconfident on out-of-distribution data, making them less trustworthy and hence reducing their suitability for practical adoption in safety-critical areas such as health and self-driving cars. In this work, we propose a capsule network based on a variational mixture of Gaussians to train distributions of network weights as opposed to a single set of weights and enable the model to express its predictive uncertainty on out-of-distribution data. Training distributions of weights have the added advantage of avoiding overfitting on smaller datasets which are common in health and other fields. Although Bayesian neural networks are known to exhibit slow training and convergence, experiment...
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Availability of massive amounts of data is a key contributing factor that influences the performance of deep learning models. Convolutional Neural Networks for instance, require large amounts of data in different variations to enable them... more
Availability of massive amounts of data is a key contributing factor that influences the performance of deep learning models. Convolutional Neural Networks for instance, require large amounts of data in different variations to enable them generalize well to viewpoints. However, in health and other application domains, data generation and processing tasks are time-consuming and requires annotation by experts. Capsule Network (CapsNet) have been proposed to curtail the limitations of Convolutional Neural Networks (CNNs). Due to the problem of crowding, capsule Networks perform badly on complex and real-life images such as CIFAR 10 and some medical images. In this study, a variant of a capsule network with a new algorithm referred to as the amplifier and a new squash function termed exponential squash is proposed. The amplifier is implemented in the encoder network to improve the texture of the images and has the ability to assign low relevance to irrelevant features and high relevance...
Research Interests:
Research Interests:
Research Interests:
The squash function in capsule networks (CapsNets) dynamic routing is less capable of performing discrimination of non-informative capsules which leads to abnormal activation value distribution of capsules. In this paper, we propose... more
The squash function in capsule networks (CapsNets) dynamic routing is less capable of performing discrimination of non-informative capsules which leads to abnormal activation value distribution of capsules. In this paper, we propose vertical squash (VSquash) to improve the original squash by preventing the activation values of capsules in the primary capsule layer to shrink non-informative capsules, promote discriminative capsules and avoid high information sensitivity. Furthermore, a new neural network, (i) skip-connected convolutional capsule (S-CCCapsule), (ii) Integrated skip-connected convolutional capsules (ISCC) and (iii) Ensemble skip-connected convolutional capsules (ESCC) based on CapsNets are presented where the VSquash is applied in the dynamic routing. In order to achieve uniform distribution of coupling coefficient of probabilities between capsules, we use the Sigmoid function rather than Softmax function. Experiments on Guangzhou Women and Children’s Medical Center (G...
Research Interests:
Brain tumor recently is considered among the deadliest cancers according to research statistics and have several categories, based on the different characteristics of the tumor. Early detection of the tumor types help to devise treatment... more
Brain tumor recently is considered among the deadliest cancers according to research statistics and have several categories, based on the different characteristics of the tumor. Early detection of the tumor types help to devise treatment plans and achieve high survival rate. Human inspection is noted to be cost effective, error prone and time-consuming, which have led the interest in Convolutional Neural Networks (CNNs) to automatize the problem. However, CNNs fail to consider the precise location of the features as beneficial, which is harmful, because tumor location and its relationship with the surrounding tissue provide high influence on the brain tumor type. In addition, the CNNs require large amount of dataset for accurate training and prediction. CNNs increasingly reduce image resolution, which result to decrease in classification accuracy. In this work, we incorporated recently developed Capsule Networks (CapsNets) which overcome these drawbacks. The focused contribution is ...
This article investigates the problem of relaxed exponential stabilization for coupled memristive neural networks (CMNNs) with connection fault and multiple delays via an optimized elastic event-triggered mechanism (OEEM). The connection... more
This article investigates the problem of relaxed exponential stabilization for coupled memristive neural networks (CMNNs) with connection fault and multiple delays via an optimized elastic event-triggered mechanism (OEEM). The connection fault of the two or some nodes can result in the connection fault of other nodes and cause iterative faults in the CMNNs. Therefore, the method of backup resources is considered to improve the fault-tolerant capability and survivability of the CMNNs. In order to improve the robustness of the event-triggered mechanism and enhance the ability of the event-triggered mechanism to process noise signals, the time-varying bounded noise threshold matrices, time-varying decreased exponential threshold functions, and adaptive functions are simultaneously introduced to design the OEEM. In addition, the appropriate Lyapunov-Krasovskii functionals (LKFs) with some improved delay-product-type terms are constructed, and the relaxed exponential stabilization and globally uniformly ultimately bounded (GUUB) conditions are derived for the CMNNs with connection fault and multiple delays by means of some inequality processing techniques. Finally, two numerical examples are provided to illustrate the effectiveness of the results.
Research Interests:
Convolutional neural networks (CNNs) for automatic classification and medical image diagnosis have recently displayed a remarkable performance. However, the CNNs fail to recognize original images rotated and oriented differently, limiting... more
Convolutional neural networks (CNNs) for automatic classification and medical image diagnosis have recently displayed a remarkable performance. However, the CNNs fail to recognize original images rotated and oriented differently, limiting their performance. This paper presents a new capsule network (CapsNet) based framework known as the multi-lane atrous feature fusion capsule network (MLAF-CapsNet) for brain tumor type classification. The MLAF-CapsNet consists of atrous and CLAHE, where the atrous increases receptive fields and maintains spatial representation, whereas the CLAHE is used as a base layer that uses an improved adaptive histogram equalization (AHE) to enhance the input images. The proposed method is evaluated using whole-brain tumor and segmented tumor datasets. The efficiency performance of the two datasets is explored and compared. The experimental results of the MLAF-CapsNet show better accuracies (93.40% and 96.60%) and precisions (94.21% and 96.55%) in feature ext...
Research Interests:
Research Interests:
This article is concerned with the exponential synchronization of coupled memristive neural networks (CMNNs) with multiple mismatched parameters and topology-based probability impulsive mechanism (TPIM) on time scales. To begin with, a... more
This article is concerned with the exponential synchronization of coupled memristive neural networks (CMNNs) with multiple mismatched parameters and topology-based probability impulsive mechanism (TPIM) on time scales. To begin with, a novel model is designed by taking into account three types of mismatched parameters, including: 1) mismatched dimensions; 2) mismatched connection weights; and 3) mismatched time-varying delays. Then, the method of auxiliary-state variables is adopted to deal with the novel model, which implies that the presented novel model can not only use any isolated system (regard as a node) in the coupled system to synchronize the states of CMNNs but also can use an external node, that is, not affiliated to the coupled system to synchronize the states of CMNNs. Moreover, the TPIM is first proposed to efficiently schedule information transmission over the network, possibly subject to a series of nonideal factors. The novel control protocol is more robust against these nonideal factors than the traditional impulsive control mechanism. By means of the Lyapunov-Krasovskii functional, robust analysis approach, and some inequality processing techniques, exponential synchronization conditions unifying the continuous-time and discrete-time systems are derived on the framework of time scales. Finally, a numerical example is provided to illustrate the effectiveness of the main results.