Deep learning in food category recognition

Information Fusion, Journal Year: 2023, Volume and Issue: 98, P. 101859 - 101859

Published: May 27, 2023

Integrating artificial intelligence with food category recognition has been a field of interest for research the past few decades. It is potentially one next steps in revolutionizing human interaction food. The modern advent big data and development data-oriented fields like deep learning have provided advancements recognition. With increasing computational power ever-larger datasets, approach's potential yet to be realized. This survey provides an overview methods that can applied various tasks, including detecting type, ingredients, quality, quantity. We core components constructing machine system recognition, augmentation, hand-crafted feature extraction, algorithms. place particular focus on learning, utilization convolutional neural networks, transfer semi-supervised learning. provide relevant studies promote further developments industrial applications.

Language: Английский

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Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 20, 2024

Language: Английский

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A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

Frontiers in Oncology, Journal Year: 2024, Volume and Issue: 14

Published: Feb. 22, 2024

With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and mortality rate this disease pose severe global health issues for women. Identifying disease’s influence is only practical way to lessen it immediately. Numerous research works have developed automated methods using different medical imaging identify BC. Still, precision each strategy differs based on available resources, issue’s nature, dataset being used. We proposed a novel deep bottleneck convolutional neural network with quantum optimization algorithm classification diagnosis from mammogram images. Two architectures named three-residual blocks four-residual bottle been parallel single paths. Bayesian Optimization (BO) has employed initialize hyperparameter values train selected dataset. Deep features are extracted average pool layer both models. After that, kernel-based canonical correlation analysis entropy technique fusion. The fused feature set further refined an generalized normal distribution optimization. finally classified several classifiers, such as bi-layered wide-neural networks. experimental process was conducted publicly INbreast, maximum accuracy 96.5% obtained. Moreover, method, sensitivity 96.45, 96.5, F1 score value 96.64, MCC 92.97%, Kappa respectively. utilized infected regions. In addition, detailed comparison few recent techniques showing framework’s higher rate.

Language: Английский

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MFFSP: Multi-scale feature fusion scene parsing network for landslides detection based on high-resolution satellite images

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 127, P. 107337 - 107337

Published: Oct. 27, 2023

Language: Английский

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Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 14, 2024

Abstract A kidney stone is a solid formation that can lead to failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing detection becomes crucial accurately classifying KUB images. This article applies transfer learning (TL) model with pre-trained VGG16 empowered explainable artificial intelligence (XAI) establish takes categorizes them as stones or normal cases. The findings demonstrate the achieves testing accuracy 97.41% in identifying X-rays dataset used. delivers highly accurate predictions but lacks fairness explainability their decision-making process. study incorporates Layer-Wise Relevance Propagation (LRP) technique, an enhance transparency effectiveness address this concern. XAI specifically LRP, increases model's transparency, facilitating comprehension predictions. play important role assisting doctors identification stones, thereby execution effective treatment strategies.

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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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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The effectiveness of deep learning vs. traditional methods for lung disease diagnosis using chest X-ray images: A systematic review

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 147, P. 110817 - 110817

Published: Sept. 9, 2023

Language: Английский

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CNN-based Approach for Enhancing Brain Tumor Image Classification Accuracy

International journal of engineering. Transactions B: Applications, Journal Year: 2024, Volume and Issue: 37(5), P. 984 - 996

Published: Jan. 1, 2024

Brain tumors are one of the deadliest diseases in world. This disease can attack anyone regardless gender or certain age groups. The diagnosis brain is carried out by manually identifying images resulting from Computerized Tomography Scan Magnetic Resonance Imaging, making it possible for diagnostic errors to occur. In addition, be made using biopsy techniques. technique very accurate but takes a long time, around 10 15 days and involves lot equipment medical personnel. Based on this, machine learning technology needed which classify based produced MRI. research aims increase accuracy previous classification so that do not occur tumors. method used this Convolutional Neural Network AlexNet Google Net architectures. results obtained an 98% architecture 96% GoogleNet. result higher when compared with research. finding reduce computational burden during model training. help physicians diagnose quickly accurately.

Language: Английский

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Automated COVID-19 detection with convolutional neural networks

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: June 30, 2023

This paper focuses on addressing the urgent need for efficient and accurate automated screening tools COVID-19 detection. Inspired by existing research efforts, we propose two framework models to tackle this challenge. The first model combines a conventional CNN architecture as feature extractor with XGBoost classifier. second utilizes classical Feedforward Neural Network classification. key distinction between lies in their classification layers. Bayesian optimization techniques are employed optimize hyperparameters of both models, enabling "cheat-start" training process optimal configurations. To mitigate overfitting, transfer learning such Dropout Batch normalization incorporated. CovidxCT-2A dataset is used training, validation, testing purposes. establish benchmark, compare performance our state-of-the-art methods reported literature. Evaluation metrics including Precision, Recall, Specificity, Accuracy, F1-score assess efficacy models. hybrid demonstrates impressive results, achieving high precision (98.43%), recall (98.41%), specificity (99.26%), accuracy (99.04%), (98.42%). standalone exhibits slightly lower but still commendable performance, (98.25%), (98.44%), (99.27%), (98.97%), (98.34%). Importantly, outperform five other terms accuracy, demonstrated results study.

Language: Английский

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