An overview of deep learning methods for multimodal medical data mining

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 200, P. 117006 - 117006

Published: April 4, 2022

Language: Английский

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Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods

Informatics in Medicine Unlocked, Journal Year: 2023, Volume and Issue: 40, P. 101286 - 101286

Published: Jan. 1, 2023

This paper investigates the applications of explainable AI (XAI) in healthcare, which aims to provide transparency, fairness, accuracy, generality, and comprehensibility results obtained from ML algorithms decision-making systems. The black box nature systems has remained a challenge interpretable techniques can potentially address this issue. Here we critically review previous studies related interpretability methods medical Descriptions various types XAI such as layer-wise relevance propagation (LRP), Uniform Manifold Approximation Projection (UMAP), Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), ANCHOR, contextual importance utility (CIU), Training calibration-based explainers (TraCE), Gradient-weighted Class Activation Mapping (Grad-CAM), t-distributed Stochastic Neighbor Embedding (t-SNE), NeuroXAI, Explainable Cumulative Fuzzy Membership Criterion (X-CFCMC) along with diseases be explained through these are provided throughout paper. also discusses how technologies transform healthcare services. usability reliability presented summarized, including on XGBoost for mediastinal cysts tumors, 3D brain tumor segmentation network, TraCE method image analysis. Overall, contribute growing field insights researchers, practitioners, decision-makers industry. Finally, discuss performance applied health care It is needed mention that brief implemented methodology section.

Language: Английский

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PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN

Biocell, Journal Year: 2022, Volume and Issue: 47(2), P. 373 - 384

Published: Nov. 18, 2022

Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to global economy and human health. It is a disease caused by severe acute respiratory syndrome 2, single-stranded RNA virus of genus Betacoronavirus. This highly infectious relies on its angiotensin-converting enzyme 2-receptor enter cells. With increase in number confirmed COVID-19 diagnoses, difficulty diagnosis due lack healthcare resources becomes increasingly apparent. Deep learning-based computer-aided models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning essential training such significantly impacts their final performance speed. However, traditional hyperparameter methods are usually time-consuming unstable. To solve issue, we introduce Particle Swarm Optimisation build PSO-guided Self-Tuning Convolution Neural Network (PSTCNN), allowing model tune hyperparameters automatically. Therefore, proposed approach reduce involvement. Also, optimisation algorithm select combination targeted manner, thus stably achieving solution closer optimum. Experimentally, PSTCNN obtain quite excellent results, sensitivity 93.65% ± 1.86%, specificity 94.32% 2.07%, precision 94.30% 2.04%, accuracy 93.99% 1.78%, F1-score 93.97% Matthews Correlation Coefficient 87.99% 3.56%, Fowlkes-Mallows Index 1.78%. Our experiments demonstrate that compared methods, using faster more effective.

Language: Английский

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Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

IEEE Reviews in Biomedical Engineering, Journal Year: 2022, Volume and Issue: 16, P. 5 - 21

Published: June 23, 2022

Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during pandemic, few have made a significant clinical impact, especially in diagnosis and disease precision staging. One major cause for such low impact is lack of model transparency, significantly limiting AI adoption real practice. To solve this problem, models need be explained users. Thus, we conducted comprehensive study Explainable (XAI) using PRISMA technology. Our findings suggest that XAI can improve performance, instill trust users, assist users decision-making. In systematic review, introduce common techniques their utility with specific examples application. We discuss evaluation results because it an important step maximizing value AI-based decision support systems. Additionally, present traditional, modern, advanced demonstrate evolution techniques. Finally, provide best practice guideline developers refer experimentation. also offer potential This hopefully, promote biomedicine healthcare.

Language: Английский

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A lightweight CNN-based network on COVID-19 detection using X-ray and CT images

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 146, P. 105604 - 105604

Published: May 11, 2022

Language: Английский

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Automated detection and forecasting of COVID-19 using deep learning techniques: A review

Neurocomputing, Journal Year: 2024, Volume and Issue: 577, P. 127317 - 127317

Published: Jan. 26, 2024

Language: Английский

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A review of deep learning in dentistry

Neurocomputing, Journal Year: 2023, Volume and Issue: 554, P. 126629 - 126629

Published: July 27, 2023

Oral diseases have a significant impact on human health, often going unnoticed in their early stages. Deep learning, promising field artificial intelligence, has shown remarkable success various domains, especially dentistry. This paper aims to provide an overview of recent research deep learning applications dentistry, with focus dental imaging. algorithms perform well difficult tasks such as image segmentation and recognition, enabling accurate identification oral conditions abnormalities. Integration other health data offers holistic understanding the relationship between systemic health. However, there are still many challenges that need be addressed.

Language: Английский

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CovH2SD: A COVID-19 detection approach based on Harris Hawks Optimization and stacked deep learning

Expert Systems with Applications, Journal Year: 2021, Volume and Issue: 186, P. 115805 - 115805

Published: Sept. 5, 2021

Language: Английский

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Application of CycleGAN and transfer learning techniques for automated detection of COVID-19 using X-ray images

Pattern Recognition Letters, Journal Year: 2021, Volume and Issue: 153, P. 67 - 74

Published: Dec. 3, 2021

Language: Английский

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A comparative study of X-ray and CT images in COVID-19 detection using image processing and deep learning techniques

Computer Methods and Programs in Biomedicine Update, Journal Year: 2022, Volume and Issue: 2, P. 100054 - 100054

Published: Jan. 1, 2022

The deadly coronavirus has not just devastated the lives of millions but put entire healthcare system under tremendous pressure. Early diagnosis COVID-19 plays a significant role in isolating positive cases and preventing further spread disease. medical images along with deep learning models provided faster more accurate results detection COVID-19. This article extensively reviews recent techniques for diagnosis. research articles discussed reveal that Convolutional Neural Network (CNN) is most popular algorithm detecting from images. An overview necessity pre-processing images, transfer data augmentation to deal scarcity problems, use pre-trained save time automatic are summarized. also provides sensible outlook young researchers develop highly effective CNN coupled early

Language: Английский

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