National-scale transportation systems are critical infrastructures to ensure the normal operation of the nation and offer essential services to modern societies. And they face a constant barrage of external stresses o...
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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 availab.e 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.
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).
Autonomous driving is a challenging scenario for image segmentation due to the presence of uncontrolled environmental conditions and the eventually catastrophic consequences of failures. Previous work suggested that a...
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NOMA (Non-Orthogonal Multiple Access), as one of the candidate technologies of 5G, can improve the spectrum efficiency and system capacity, and has attracted wide attention. The essence of NOMA is multi-user overlay t...
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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 availab.e 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 availab.e to showcase the productiveness of the obtained criterion.
Few-shot learning aims for rapid adaptation with few samples. Recently, cross-granularity few-shot learning has emerged as a promising research area, where models observe coarse lab.ls but target fine-grained recognit...
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Few-shot learning aims for rapid adaptation with few samples. Recently, cross-granularity few-shot learning has emerged as a promising research area, where models observe coarse lab.ls but target fine-grained recognition among novel classes. As coarse supervision tends to eliminate feature discrimination among underlying sub-classes, existing methods commonly utilize self-supervision as a complement to explore intra-class variation. However, current methods suffer from an intrinsic conflict between contrastive learning and coarse supervision. In this paper, we locate the root cause of the intrinsic conflict. Then, we resolve it by exploiting the similarity among augmented views while ignoring the unreasonable constraint between negative pairs. Besides, we decouple contrastive learning and coarse supervision into parallel branches to better regularize the latent space. Albeit simple, our approach consistently outperforms state-of-the-art methods across different benchmarks.
Cancer-associated biomarker genes play an indispensable role in the intricate tapestry of cancer development and manifestation. The expression of biomarkers in different types of tumor cells has beneficial implication...
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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.
Smoke has a very bad effect on the outdoor vision system. Not only are the videos with poor visual effects obtained, but also the quality and structure of the videos are reduced. In this paper, we propose a video smok...
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