Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 223, P. 119900 - 119900

Published: March 18, 2023

Language: Английский

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A novel lightweight deep learning model based on SqueezeNet architecture for viral lung disease classification in X-ray and CT images

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2024, Volume and Issue: 10(4)

Published: Oct. 8, 2024

COVID-19 has affected hundreds of millions individuals, seriously harming the global population’s health, welfare, and economy. Furthermore, health facilities are severely overburdened due to record number cases, which makes prompt accurate diagnosis difficult. Automatically identifying infected individuals promptly placing them under special care is a critical step in reducing burden such issues. Convolutional Neural Networks (CNN) other machine learning techniques can be utilized address this demand. Many existing Deep models, albeit producing intended outcomes, were developed using parameters, making unsuitable for use on devices with constrained resources. Motivated by fact, novel lightweight deep model based Efficient Channel Attention (ECA) module SqueezeNet architecture, work identify patients from chest X-ray CT images initial phases disease. After proposed was tested different datasets two, three four classes, results show its better performance over models. The outcomes shown that, comparison current heavyweight our models reduced cost memory requirements computing resources dramatically, while still achieving comparable performance. These support notion that help diagnose Covid-19 being easily implemented low-resource low-processing devices.

Language: Английский

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Innovative applications of artificial intelligence during the COVID-19 pandemic

Infectious Medicine, Journal Year: 2024, Volume and Issue: 3(1), P. 100095 - 100095

Published: Feb. 21, 2024

The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of management and response. In the present review, we discuss possibilities AI technology in addressing global posed by pandemic. First, outline multiple impacts current on public health, economy, society. Next, focus innovative applications advanced areas such as prediction, detection, control, drug discovery treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, omics data to forecast disease spread patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems support risk assessment, decision-making, social sensing, thereby improving epidemic control health policies. Furthermore, high-throughput virtual screening enables accelerate identification therapeutic candidates opportunities repurposing. Finally, future research directions combating COVID-19, emphasizing importance interdisciplinary collaboration. Though promising, barriers related model generalization, quality, infrastructure readiness, ethical risks must be addressed fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise stakeholders is imperative developing robust, responsible, human-centered solutions against emergencies.

Language: Английский

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CNN-IKOA: convolutional neural network with improved Kepler optimization algorithm for image segmentation: experimental validation and numerical exploration

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Jan. 10, 2024

Abstract Chest diseases, especially COVID-19, have quickly spread throughout the world and caused many deaths. Finding a rapid accurate diagnostic tool was indispensable to combating these diseases. Therefore, scientists thought of combining chest X-ray (CXR) images with deep learning techniques rapidly detect people infected COVID-19 or any other disease. Image segmentation as preprocessing step has an essential role in improving performance techniques, it could separate most relevant features better train techniques. several approaches were proposed tackle image problem accurately. Among methods, multilevel thresholding-based methods won significant interest due their simplicity, accuracy, relatively low storage requirements. However, increasing threshold levels, traditional failed achieve segmented reasonable amount time. researchers recently used metaheuristic algorithms this problem, but existing still suffer from slow convergence speed stagnation into local minima number levels increases. study presents alternative technique based on enhanced version Kepler optimization algorithm (KOA), namely IKOA, segment CXR at small, medium, high levels. Ten are assess IKOA ten (T-5, T-7, T-8, T-10, T-12, T-15, T-18, T-20, T-25, T-30). To observe its effectiveness, is compared terms indicators. The experimental outcomes disclose superiority over all algorithms. Furthermore, IKOA-based eight different newly CNN model called CNN-IKOA find out effectiveness step. Five indicators, overall precision, recall, F1-score, specificity, CNN-IKOA’s effectiveness. CNN-IKOA, according outcomes, outstanding for where reach 94.88% 96.57% 95.40% recall.

Language: Английский

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Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(6), P. 3267 - 3301

Published: Feb. 19, 2024

Language: Английский

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Enhancing coffee bean classification: a comparative analysis of pre-trained deep learning models

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(16), P. 9023 - 9052

Published: April 1, 2024

Abstract Coffee bean production can encounter challenges due to fluctuations in global coffee prices, impacting the economic stability of some countries that heavily depend on production. The primary objective is evaluate how effectively various pre-trained models predict types using advanced deep learning techniques. selection an optimal model crucial, given growing popularity specialty and necessity for precise classification. We conducted a comprehensive comparison several models, including AlexNet, LeNet, HRNet, Google Net, Mobile V2 ResNet (50), VGG, Efficient, Darknet, DenseNet, utilizing coffee-type dataset. By leveraging transfer fine-tuning, we assess generalization capabilities classification task. Our findings emphasize substantial impact choice model's performance, with certain demonstrating higher accuracy faster convergence than conventional alternatives. This study offers thorough evaluation architectural regarding their effectiveness Through result metrics, sensitivity (1.0000), specificity (0.9917), precision (0.9924), negative predictive value F1 score (0.9962), our analysis provides nuanced insights into intricate landscape models.

Language: Английский

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Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture

Journal of Applied Biomedicine, Journal Year: 2023, Volume and Issue: 43(3), P. 528 - 550

Published: June 26, 2023

Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly medically vulnerable patients. In last few decades, new types of lung-related have taken lives millions people, COVID-19 has almost 6.27 million lives. To fight against diseases, timely correct diagnosis with appropriate treatment is crucial in current pandemic. this study, an intelligent recognition system seven been proposed based on machine learning (ML) techniques aid medical experts. Chest X-ray (CXR) images were collected from publicly available databases. A lightweight convolutional neural network (CNN) used extract characteristic features raw pixel values CXR images. The best feature subset identified using Pearson Correlation Coefficient (PCC). Finally, extreme (ELM) perform classification task assist faster reduced computational complexity. CNN-PCC-ELM model achieved accuracy 96.22% Area Under Curve (AUC) 99.48% eight class classification. outcomes demonstrated better performance than existing state-of-the-art (SOTA) models case COVID-19, detection both binary multiclass classifications. For classification, precision, recall fi-score ROC are 100%, 99%, 100% 99.99% respectively demonstrating its robustness. Therefore, overshadowed pioneering accurately differentiate other that can physicians treating patient effectively.

Language: Английский

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Advancing task recognition towards artificial limbs control with ReliefF-based deep neural network extreme learning

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 169, P. 107894 - 107894

Published: Dec. 22, 2023

Language: Английский

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A novel lightweight CNN for chest X-ray-based lung disease identification on heterogeneous embedded system

Applied Intelligence, Journal Year: 2024, Volume and Issue: 54(6), P. 4756 - 4780

Published: March 1, 2024

Abstract The global spread of epidemic lung diseases, including COVID-19, underscores the need for efficient diagnostic methods. Addressing this, we developed and tested a computer-aided, lightweight Convolutional Neural Network (CNN) rapid accurate identification diseases from 29,131 aggregated Chest X-ray (CXR) images representing seven disease categories. Employing five-fold cross-validation method to ensure robustness our results, CNN model, optimized heterogeneous embedded devices, demonstrated superior performance. It achieved 98.56% accuracy, outperforming established networks like ResNet50, NASNetMobile, Xception, MobileNetV2, DenseNet121, ViT-B/16 across precision, recall, F1-score, AUC metrics. Notably, model requires significantly less computational power only 55 minutes average training time per fold, making it highly suitable resource-constrained environments. This study contributes developing efficient, in medical image analysis, underscoring their potential enhance point-of-care processes.

Language: Английский

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Automated detection of COVID-19 and pneumonia diseases using data mining and transfer learning algorithms with focal loss from chest X-ray images

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 162, P. 111806 - 111806

Published: May 25, 2024

Language: Английский

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