Machine learning based technique to predict the water adulterant in milk using portable near infrared spectroscopy

Journal of Food Composition and Analysis, Journal Year: 2024, Volume and Issue: 131, P. 106270 - 106270

Published: April 22, 2024

Language: Английский

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Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 185, P. 109494 - 109494

Published: Dec. 4, 2024

Language: Английский

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Radon transform-based improved single seeded region growing segmentation for lung cancer detection using AMPWSVM classification approach

Signal Image and Video Processing, Journal Year: 2023, Volume and Issue: 17(8), P. 4571 - 4580

Published: Aug. 19, 2023

Language: Английский

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Enhancing fish freshness prediction using NasNet-LSTM

Journal of Food Composition and Analysis, Journal Year: 2023, Volume and Issue: 127, P. 105945 - 105945

Published: Dec. 23, 2023

Language: Английский

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An extensive investigation of Convolutional Neural Network designs for the diagnosis of Lumpy skin disease in Dairy Cows

Heliyon, Journal Year: 2024, Volume and Issue: 10(14), P. e34242 - e34242

Published: July 1, 2024

Cow diseases are a major source of concern for people. Some in animals that discovered their early stages can be treated while they still treatable. If lumpy skin disease (LSD) is not properly treated, it result significant financial losses the farm animal industry. Animals like cows sign this have seriously affected. A reduction milk production, reduced fertility, growth retardation, miscarriage, and occasionally death all detrimental effects cows. Over past three months, LSD has affected thousands cattle nearly fifty districts across Bangladesh, causing farmers to worry about livelihood. Although virus very contagious, after receiving right care few cured. The goal study was use various deep learning machine models determine whether or had disease. To accomplish work, Convolution neural network (CNN) based novel architecture proposed detecting illness. disease-affected area been identified using image preprocessing segmentation techniques. After extraction numerous features, our model evaluated classify LSD. Four CNN models, DenseNet, MobileNetV2, Xception, InceptionResNetV2 were used framework, evaluation metrics computed how well classifiers worked. MobileNetV2 able achieve 96% classification accuracy an AUC score 98% by comparing results with recently published relevant works, which seems both good promising.

Language: Английский

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A method of maize seed variety identification based on near-infrared spectroscopy combined with improved DenseNet model

Microchemical Journal, Journal Year: 2024, Volume and Issue: 206, P. 111542 - 111542

Published: Sept. 1, 2024

Language: Английский

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Attention-guided CenterNet deep learning approach for lung cancer detection

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 186, P. 109613 - 109613

Published: Jan. 2, 2025

Language: Английский

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Advanced Hybridization and Optimization of DNNs for Medical Imaging: A Survey on Disease Detection Techniques

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(4)

Published: Feb. 4, 2025

Language: Английский

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Evaluating pre-processing and deep learning methods in medical imaging: Combined effectiveness across multiple modalities

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 119, P. 558 - 586

Published: Feb. 10, 2025

Language: Английский

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Deep Learning Innovations in the Detection of Lung Cancer: Advances, Trends, and Open Challenges

Cognitive Computation, Journal Year: 2025, Volume and Issue: 17(2)

Published: Feb. 17, 2025

Language: Английский

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