In recent years, research in automatic Sign Language Recognition (SLR) has undergone significant progress, serving as a founda-tional base for developing applications that aim to promote the integration of deaf indivi...
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In recent years, research in automatic Sign Language Recognition (SLR) has undergone significant progress, serving as a founda-tional base for developing applications that aim to promote the integration of deaf indivi...
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In recent years, research in automatic Sign Language Recognition (SLR) has undergone significant progress, serving as a founda-tional base for developing applications that aim to promote the integration of deaf individuals into society. Most of this progress is owed to the recent developments in deep learning. However, the deployment of conventional Artificial Neural Networks (ANNs) can be hindered by their requirements in terms of computational power and energy consumption. Therefore, to improve the ef-ciency of current SLR systems, in this work, we propose the use of the increasingly popular Spiking Neural Networks (SNNs), which, on the one hand, provide more energy-efficient computations than conventional ANNs and, on the other hand, are able to process temporal sequences with simpler architectures thanks to their temporal dynamics. To evaluate our method, we utilize WLASL300, the 300-word (300 classes of signs) dataset from Word-Level American Sign Language, and achieve an improvement in accuracy with the SNN (+2.70%) over the previous state-of-the-art, when working with energy-efficient spiking neurons. Furthermore, we construct a non-spiking version of the same network and evaluate it in a similar manner. Our results demonstrate how the SNN has sparser activations (25% less), thanks to the use of spiking neurons, and therefore can be implemented with a lower power requirement than an ANN version of the same architecture. This work thus demonstrates the possibility of performing SLR in a very effective and efficient way, thus opening up the development of applications that span from the automatic real-time translation of dynamic signs to remote control utilizing sign languages.
Accurate and timely diagnosis of liver disorders such as fatty liver disease, chronic viral hepatitis, and excessive alcohol consumption is crucial for maintaining liver health. Traditional methods for liver screening...
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ISBN:
(数字)9798331531539
ISBN:
(纸本)9798331531546
Accurate and timely diagnosis of liver disorders such as fatty liver disease, chronic viral hepatitis, and excessive alcohol consumption is crucial for maintaining liver health. Traditional methods for liver screening are often subjective, time-consuming, and reliant on the expertise of the sonographer, which can impact diagnostic accuracy. To tackle these challenges, in this paper proposed a deep learning (DL) based framework to enhance the effective diagnosis of focal liver lesions. This approach leverages an channel attention mechanism integrated with the DarkNet-19 pre-trained model to improve feature extraction and boosts classification accuracy. By automating the diagnostic process, the proposed model addresses the limitations of traditional methods, providing a more efficient and reliable solution for liver disorder diagnosis. Experimental results with an ultrasound image dataset demonstrate that the proposed model significantly outperforms conventional DL methods, showcasing its advantages in performance and efficiency.
Among various types of skin cancers, melanoma is the most aggressive and deadly. There is a notable growth in the implementation of deep learning (DL) methods to identify skin malignancies in dermoscopy images. This p...
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ISBN:
(数字)9798350350845
ISBN:
(纸本)9798350350852
Among various types of skin cancers, melanoma is the most aggressive and deadly. There is a notable growth in the implementation of deep learning (DL) methods to identify skin malignancies in dermoscopy images. This paper introduces a lightweight DL-based approach designed for seamless integration into low-memory devices within healthcare applications. The proposed method incorporates three lightweight convolutional neural network (CNN) models: MobileNet-v2, SqueezeNet, and GoogLeNet. Initially, test features are computed from fine-tuned deep CNN models. Subsequently, probability scores for each class are derived by training and testing a random forest classifier with features extracted from the models. Then, the proposed method uses an average ensemble voting technique on the probability scores to enhance the classification performance compared to the individual models. The proposed of lightweight CNN model demonstrated an accuracy of 85.19 % which is better than existing works.
Breast cancer (BC) is a potentially life-threatening disease that occurs because of uncontrolled growth of abnormal corpuscles in the breast tissue. Pathologists analyze the tissue structures using histopathological w...
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ISBN:
(数字)9798350350951
ISBN:
(纸本)9798350350968
Breast cancer (BC) is a potentially life-threatening disease that occurs because of uncontrolled growth of abnormal corpuscles in the breast tissue. Pathologists analyze the tissue structures using histopathological whole slide images to identify cancerous anomalies. However, pathologists face severe challenges such as fatigue, subjectivity, and inter-observer variability in the early detection of BC. Understanding the intricacies of BC from molecular tissue structures is complex, and inexpertise leads to adverse outcomes. This paper proposes a computed aided detection (CAD) system that can assist histopathologists in the early detection of BC, potentially reducing the abnormalities and diagnostic time. Leveraging the power of convolutional neural networks (CNNs), a stacked ensemble-based model is developed to identify benign and malignant cancerous tissues using histopathological images. The ensemble models comprise three deep CNNs, namely MobileNetV2, ShuffleNet, and SqueezeNet, trained on the BreakHis dataset. Finally, individual CNNs predictions are fed to the average voting-based classifier to identify benign and malignant tissues. The stacked ensemble-based deep CNN model outperformed the individual CNN models in BC prediction, achieving superior accuracy and robustness.
This paper investigates the problem of local stability for fixed-point interfered digital filters with generalized overflow nonlinearities. First, a familiar form of overflow nonlinearity function, covering the nonlin...
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This paper investigates the problem of local stability for fixed-point interfered digital filters with generalized overflow nonlinearities. First, a familiar form of overflow nonlinearity function, covering the nonlinearities like zeroing, saturation, two's complement, and triangular is established. Second, the asymptotic stability condition for the digital filters is formulated in local context without external interference. Third, with external interference the work is extended to investigate the local stability and to attain H ∞ performance of the digital filter using generalized nonlinearity function. Further, the conventional global stability results can be established as special cases of presented local approach. Finally, sufficient numerical examples are presented to highlight the merit of proposed approach.
Fundus imaging is a valuable diagnostic tool in ophthalmology, providing clinicians with detailed visualizations of the retina and aiding in the detection and monitoring of various eye diseases, including age-related ...
Fundus imaging is a valuable diagnostic tool in ophthalmology, providing clinicians with detailed visualizations of the retina and aiding in the detection and monitoring of various eye diseases, including age-related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), and cataract. However, the quality of fundus images can be significantly affected by noise, mainly additive white Gaussian noise (AWGN), which is inherent in many imaging systems. The presence of noise in real-world data poses significant challenges for computer vision tasks. In the field of medical image classification, a wrong diagnoisis has heavy consequences. Understanding the impact of AWGN on fundus images is crucial for developing practical denoising algorithms and improving diagnostic accuracy. This work presents an analysis of AWGN noise in fundus images aims to characterize its effects on image quality and assess its impact on diagnostic tasks. The work also analyzes the performance of six models (3 each) of two popular deep learning architectures, Convolutional Neural Networks (CNN) and Vision Transformers (ViT) in the presence of AWGN. AWGN is first introduced to the clean image datasets to conduct the analysis. The CNN and ViT models are trained on the noisy datasets to evaluate the performance of the image classification task. The work also involves six denoising algorithms and a popular image enhancement algorithm- Contrast Limited Adaptive Histogram Equalization (CLAHE).
Anemia is a common medical condition affecting millions worldwide, particularly in developing countries. Early detection of anemia is crucial for prompt treatment and prevention of its potential complications. In rece...
Anemia is a common medical condition affecting millions worldwide, particularly in developing countries. Early detection of anemia is crucial for prompt treatment and prevention of its potential complications. In recent years, deep learning (DL) has shown great potential in various medical applications, including medical image classification, anomaly detection, and segmentation. This study proposes a transfer learning-based approach using a pre-trained DL model to detect anemia from palpebral conjunctiva images. The proposed method utilizes a pre-trained DenseNet-201 model and fine-tuned it on a target dataset of palpebral conjunctiva images to detect anemia. Deep features of palpebral conjunctiva images computed from the fine-tuned DenseNet-201 are fed to MLP to identify anemia. The performance of the proposed method is evaluated on a publicly available anemia dataset, and the results show that the proposed method achieves an accuracy of 93.7 % in detecting anemia from palpebral conjunctiva images. In addition to anemia classification, we computed the hemoglobin level of palpebral conjunctiva images based on the gray-level co-occurrence matrix (GLCM) statistical properties. The statistical properties of GLCM are given to support vector and polynomial regressors, and the mean value of the predicted scores of both regressors is used to estimate the hemoglobin level. Experimental results show that the proposed model achieves an average root mean square error of 0.72 for conjunctiva images.
This article deals with stability investigation of state-space fixed-point discrete system using saturation nonlinearity and external interference. A criterion for exponential stability is proposed via a passivity-bas...
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ISBN:
(数字)9798350310252
ISBN:
(纸本)9798350310269
This article deals with stability investigation of state-space fixed-point discrete system using saturation nonlinearity and external interference. A criterion for exponential stability is proposed via a passivity-based approach for an externally interfered nonlinear discrete system. For the discrete system with saturation overflow nonlinearity, we have analyzed the passive behaviour under the effects of external interference and asymptotic stability with zero interference. A comparative study is made available with the previously reported result to highlight the worth and relaxed nature of the proposed work. The criterion developed is in linear matrix inequality (LMI) settings and therefore, numerically less complex. Numerical simulations are made available to showcase the productiveness of the obtained criterion.
Anemia, characterized by a deficiency in red blood corpuscles or hemoglobin, poses a significant global health challenge, particularly affecting vulnerable populations. Traditional diagnostic methods often involve inv...
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ISBN:
(数字)9798350350951
ISBN:
(纸本)9798350350968
Anemia, characterized by a deficiency in red blood corpuscles or hemoglobin, poses a significant global health challenge, particularly affecting vulnerable populations. Traditional diagnostic methods often involve invasive procedures, posing challenges in resource-limited settings. This study aims to explore non-invasive anemia detection using fingernail images and convolutional neural networks (CNNs) as a promising alternative to conventional diagnostic approaches. The study utilizes a dataset of fingernail images collected from hospitals in Ghana, comprising both anemic and non-anemic cases. The dataset undergoes preprocessing, including selective enhancement of red components, conversion to the CIElab color space, and feature extraction. A multi-input Deep Neural Network (DNN) framework employing pre-trained CNNs is proposed for anemia classification. The pre-trained CNN architectures include EfficientNet B1, EfficientNet B4, and MobileNet V3. The framework’s performance was assessed using two methodologies: The first involved random shuffling of the dataset, followed by division into training, testing, and validation sets, with evaluation metrics including Accuracy, Precision, F1 scores, and a Confusion Matrix. The second employed five-fold cross-validation, measured using accuracy. The evaluation of the proposed DNN framework using both of the methodologies indicates that EfficientNet B4 achieved the highest testing accuracy (97.87%), precision (97.88%), recall (97.87%), and F1 score (97.88%) and a cross-validation accuracy of 97.37% for the first and second methodologies respectively making it best fit for the proposed DNN framework. The findings demonstrate that the proposed framework yields promising results, especially under the second approach, and opens avenues for further exploration in transfer learning, fine-tuning of deep neural networks for multi-input feature integration, and cross-validation.
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