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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Interpretable COVID-19 chest X-ray detection based on handcrafted feature analysis and sequential neural network

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 186, P. 109659 - 109659

Published: Jan. 22, 2025

Language: Английский

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Attention induced multi-head convolutional neural network organization with MobileNetv1 transfer learning and COVID-19 diagnosis using jellyfish search optimization process on chest X-ray images

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 93, P. 106133 - 106133

Published: March 2, 2024

Language: Английский

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IncV3-BLSTM: a multi-label inceptionV3-BLSTM model for predicting potential side effects of COVID-19 drugs

Annals of Mathematics and Artificial Intelligence, Journal Year: 2025, Volume and Issue: unknown

Published: March 31, 2025

Language: Английский

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Computer Vision and Transfer Learning for Grading of Egyptian Cotton Fibres

AgriEngineering, Journal Year: 2025, Volume and Issue: 7(5), P. 127 - 127

Published: April 22, 2025

Egyptian cotton fibres have worldwide recognition due to their distinct quality and luxurious textile products known by the “Egyptian Cotton“ label. However, fibre trading in Egypt still depends on human grading of quality, which is resource-intensive faces challenges terms subjectivity expertise requirements. This study investigates colour vision transfer learning classify grade five long (Giza 86, Giza 90, 94) extra-long 87 96) staple cultivars. Five Convolutional Neural networks (CNNs)—AlexNet, GoogleNet, SqueezeNet, VGG16, VGG19—were fine-tuned, optimised, tested independent datasets. The highest classifications were 75.7%, 85.0%, 80.0%, 77.1%, 90.0% for 87, 94, 96, respectively, with F1-Scores ranging from 51.9–100%, 66.7–100%, 42.9–100%, 40.0–100%, 80.0–100%. Among CNNs, AlexNet, VGG19 outperformed others. Fused CNN models further improved classification accuracy up 7.2% all cultivars except 87. These results demonstrate feasibility developing a fast, low-cost, low-skilled system that overcomes inconsistencies limitations manual early stages Egypt.

Language: Английский

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Metaheuristic optimizers integrated with vision transformer model for severity detection and classification via multimodal COVID-19 images

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 22, 2025

Language: Английский

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TfrAdmCov: a robust transformer encoder based model with Adam optimizer algorithm for COVID-19 mutation prediction

Connection Science, Journal Year: 2024, Volume and Issue: 36(1)

Published: June 12, 2024

The development of vaccines and drugs is very important in combating the coronavirus disease 2019 (COVID-19) virus. effectiveness these developed has decreased as a result mutation COVID-19 Therefore, it to combat mutations. majority studies published literature are other than prediction. We focused on this gap study. This study proposes robust transformer encoder based model with Adam optimizer algorithm called TfrAdmCov for Our main motivation predict mutations occurring virus using proposed model. experimental results have shown that outperforms both baseline models several state-of-the-art models. reached accuracy 99.93%, precision 100.00%, recall 97.38%, f1-score 98.67% MCC 98.65% testing dataset. Moreover, evaluate performance model, we carried out prediction influenza A/H3N2 HA obtained promising drugs.

Language: Английский

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A novel soft computing based efficient feature selection approach for timely identification of COVID-19 infection using chest computed tomography images: a human centered intelligent clinical decision support system

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: June 12, 2024

Language: Английский

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Principles of Machine Learning

Published: Jan. 1, 2024

Language: Английский

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Intelligent computing framework to analyze the transmission risk of COVID-19: Meyer wavelet artificial neural networks

Computational Biology and Chemistry, Journal Year: 2024, Volume and Issue: 113, P. 108234 - 108234

Published: Oct. 2, 2024

Language: Английский

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