A systematic literature review on deep learning approaches for pneumonia detection using chest X-ray images

Multimedia Tools and Applications, Год журнала: 2023, Номер 83(8), С. 24101 - 24151

Опубликована: Авг. 9, 2023

Язык: Английский

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A comprehensive review on federated learning based models for healthcare applications

Artificial Intelligence in Medicine, Год журнала: 2023, Номер 146, С. 102691 - 102691

Опубликована: Окт. 30, 2023

Язык: Английский

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AI student success predictor: Enhancing personalized learning in campus management systems

Computers in Human Behavior, Год журнала: 2024, Номер 158, С. 108301 - 108301

Опубликована: Май 13, 2024

Язык: Английский

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A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

Frontiers in Genetics, Год журнала: 2023, Номер 14

Опубликована: Июль 20, 2023

Accurate diagnosis is the key to providing prompt and explicit treatment disease management. The recognized biological method for molecular of infectious pathogens polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes diagnosis, prognosis, treatment. models reduce time cost used by wet-lab experimental procedures. Consequently, sophisticated computational have been developed facilitate detection cancer, leading cause death globally, other complex diseases. In this review, we systematically evaluate recent trends multi-omics data analysis based on techniques their application prediction. We highlight current challenges field discuss how advances methods optimization overcoming them. Ultimately, review promotes development novel deep-learning methodologies integration, which essential

Язык: Английский

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A New Dawn for Tomato-spotted wilt virus Detection and Intensity Classification: A CNN and LSTM Ensemble Model

Опубликована: Май 26, 2023

Tomato-spotted wilt virus (TSWV) is a severe plant disease that causes significant economic losses in tomato production worldwide. Early detection and intensity classification of TSWV-infected plants critical for effective management. This study proposes novel TSWV approach based on convolutional neural network (CNN) long short-term memory (LSTM) ensemble model. A dataset comprising 30,000 images infected with was gathered annotated six levels, ranging from 0 (indicating no symptoms) to 5 symptoms). framework developed, aiming enhancing the model’s performance r proposed achieved an overall accuracy 97.37% test set, outperforming several state-of-the-art approaches. We also performed statistical analysis inter-intensity level variability found increased level. Our results suggest has potential be used early plants, which could aid timely application preventive measures reduce caused by TSWV.

Язык: Английский

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A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble

Diagnostics, Год журнала: 2024, Номер 14(4), С. 390 - 390

Опубликована: Фев. 11, 2024

In the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a significant stride in efficacy medical decision systems. This paper presents novel approach utilizing convolutional neural network that effectively amalgamates strengths EfficientNetB0 and DenseNet121, it is enhanced by suite attention mechanisms for refined classification. Leveraging pre-trained models, our employs multi-head, self-attention modules meticulous feature extraction from images. The model’s integration processing efficiency are further augmented channel-attention-based fusion strategy, one complemented residual block an attention-augmented enhancement dynamic pooling strategy. Our used dataset, which comprises comprehensive collection chest images, represents both healthy individuals those affected pneumonia, serves as foundation this research. study delves into algorithms, architectural details, operational intricacies proposed model. empirical outcomes model noteworthy, with exceptional performance marked accuracy 95.19%, precision 98.38%, recall 93.84%, F1 score 96.06%, specificity 97.43%, AUC 0.9564 on test dataset. These results not only affirm high diagnostic accuracy, but also highlight its promising potential real-world clinical deployment.

Язык: Английский

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OPT-CO: Optimizing pre-trained transformer models for efficient COVID-19 classification with stochastic configuration networks

Information Sciences, Год журнала: 2024, Номер 680, С. 121141 - 121141

Опубликована: Июль 8, 2024

Building upon pre-trained ViT models, many advanced methods have achieved significant success in COVID-19 classification. Many scholars pursue better performance by increasing model complexity and parameters. While these can enhance performance, they also require extensive computational resources extended training times. Additionally, the persistent challenge of overfitting, due to limited dataset sizes, remains a hurdle. To address challenges, we proposed novel method optimize transformer models for efficient classification with stochastic configuration networks (SCNs), referred as OPT-CO. We two optimization methods: sequential (SeOp) parallel (PaOp), incorporating optimizers manner, respectively. Our without necessitating parameter expansion. introduced OPT-CO-SCN avoid overfitting problems through adoption random projection head augmentation. The experiments were carried out evaluate our based on publicly available datasets. Based evaluation results, superior, surpassing other state-of-the-art methods.

Язык: Английский

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Computer aided diagnosis using Harris Hawks optimizer with deep learning for pneumonia detection on chest X-ray images

International Journal of Information Technology, Год журнала: 2024, Номер 16(3), С. 1677 - 1683

Опубликована: Янв. 18, 2024

Язык: Английский

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Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model

IEEE Access, Год журнала: 2024, Номер 12, С. 34691 - 34707

Опубликована: Янв. 1, 2024

Pneumonia is a potentially life-threatening infectious disease that typically diagnosed through physical examinations and diagnostic imaging techniques such as chest X-rays, ultrasounds, or lung biopsies. Accurate diagnosis crucial wrong diagnosis, inadequate treatment lack of can cause serious consequences for patients may become fatal. The advancements in deep learning have significantly contributed to aiding medical experts diagnosing pneumonia by assisting their decision-making process. By leveraging models, healthcare professionals enhance accuracy make informed decisions suspected having pneumonia. In this study, six models including CNN, InceptionResNetV2, Xception, VGG16, ResNet50, Efficient-NetV2L are implemented evaluated. study also incorporates the Adam optimizer, which effectively adjusts epoch all models. trained on dataset 5856 X-ray images show 87.78%, 88.94%, 90.7%, 91.66%, 87.98%, 94.02% ResNet50 EfficientNetV2L, respectively. Notably, EfficientNetV2L demonstrates highest proves its robustness detection. These findings highlight potential accurately detecting predicting based images, providing valuable support clinical improving patient treatment.

Язык: 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, Год журнала: 2024, Номер 31(6), С. 3267 - 3301

Опубликована: Фев. 19, 2024

Язык: Английский

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