Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022)

Computer Methods and Programs in Biomedicine, Год журнала: 2022, Номер 226, С. 107161 - 107161

Опубликована: Сен. 27, 2022

Язык: Английский

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A Deep Learning based model for the Detection of Pneumonia from Chest X-Ray Images using VGG-16 and Neural Networks

Procedia Computer Science, Год журнала: 2023, Номер 218, С. 357 - 366

Опубликована: Янв. 1, 2023

Pneumonia is a viral infection which affects significant proportion of individuals, especially in developing and penurious countries where contamination, overcrowded, unsanitary living conditions are widespread, along with the lack healthcare infrastructures. produces pericardial effusion, disease wherein fluids fill chest create inhaling problems. It difficult step to recognize presence pneumonia quickly order receive treatment services improve survival chances. Deep learning, field artificial intelligence used successful development prediction models. There various ways detecting such as CT-scan, pulse oximetry, many more among most common way X-ray tomography. On other hand, examining X-rays (CXR) tough process susceptible subjective variability. In this work, deep learning(DL) model using VGG16 utilized for classifying two CXR image datasets. The Neural Networks (NN) provides an accuracy value 92.15%, recall 0.9308, precision 0.9428, F1-Score0.937 first dataset. Furthermore, experiment NN has been performed on another dataset containing 6,436 images pneumonia, normal covid-19. results second provide accuracy, recall, precision, F1-score 95.4%, 0.954, respectively. research outcome exhibits that better performance than Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Naïve Bayes (NB) both Further, proposed work exhibit improved datasets 1 2 comparison existing

Язык: Английский

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Machine learning applications for COVID-19 outbreak management

Neural Computing and Applications, Год журнала: 2022, Номер 34(18), С. 15313 - 15348

Опубликована: Июнь 10, 2022

Язык: Английский

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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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Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

IEEE/CAA Journal of Automatica Sinica, Год журнала: 2023, Номер 10(4), С. 859 - 876

Опубликована: Март 28, 2023

Artificial intelligence (AI) continues to transform data analysis in many domains. Progress each domain is driven by a growing body of annotated data, increased computational resources, and technological innovations. In medicine, the sensitivity complexity tasks, potentially high stakes, requirement accountability give rise particular set challenges. this review, we focus on three key methodological approaches that address some challenges AI-driven medical decision making. 1) Explainable AI aims produce human-interpretable justification for output. Such models increase confidence if results appear plausible match clinicians expectations. However, absence explanation does not imply an inaccurate model. Especially highly non-linear, complex are tuned maximize accuracy, such interpretable representations only reflect small portion justification. 2) Domain adaptation transfer learning enable be trained applied across multiple For example, classification task based images acquired different acquisition hardware. 3) Federated enables large-scale without exposing sensitive personal health information. Unlike centralized learning, where machine has access entire training federated process iteratively updates sites exchanging parameter updates, data. This narrative review covers basic concepts, highlights relevant corner-stone state-of-the-art research field, discusses perspectives.

Язык: Английский

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Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images

Expert Systems with Applications, Год журнала: 2023, Номер 223, С. 119900 - 119900

Опубликована: Март 18, 2023

Язык: Английский

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A Deep Learning Model for Early Prediction of Pneumonia Using VGG19 and Neural Networks

Lecture notes in networks and systems, Год журнала: 2023, Номер unknown, С. 597 - 612

Опубликована: Янв. 1, 2023

Язык: Английский

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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, Год журнала: 2024, Номер 10(4)

Опубликована: Окт. 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.

Язык: Английский

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AI-Driven Advances in Low-Dose Imaging and Enhancement—A Review

Diagnostics, Год журнала: 2025, Номер 15(6), С. 689 - 689

Опубликована: Март 11, 2025

The widespread use of medical imaging techniques such as X-rays and computed tomography (CT) has raised significant concerns regarding ionizing radiation exposure, particularly among vulnerable populations requiring frequent imaging. Achieving a balance between high-quality diagnostic minimizing exposure remains fundamental challenge in radiology. Artificial intelligence (AI) emerged transformative solution, enabling low-dose protocols that enhance image quality while significantly reducing doses. This review explores the role AI-assisted imaging, CT, X-ray, magnetic resonance (MRI), highlighting advancements deep learning models, convolutional neural networks (CNNs), other AI-based approaches. These technologies have demonstrated substantial improvements noise reduction, artifact removal, real-time optimization parameters, thereby enhancing accuracy mitigating risks. Additionally, AI contributed to improved radiology workflow efficiency cost reduction by need for repeat scans. also discusses emerging directions AI-driven including hybrid systems integrate post-processing with data acquisition, personalized tailored patient characteristics, expansion applications fluoroscopy positron emission (PET). However, challenges model generalizability, regulatory constraints, ethical considerations, computational requirements must be addressed facilitate broader clinical adoption. potential revolutionize safety, optimizing quality, improving healthcare efficiency, paving way more advanced sustainable future

Язык: Английский

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An optimized fuzzy deep learning model for data classification based on NSGA-II

Neurocomputing, Год журнала: 2022, Номер 522, С. 116 - 128

Опубликована: Дек. 14, 2022

Язык: Английский

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