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

Diagnostics, Journal Year: 2025, Volume and Issue: 15(6), P. 689 - 689

Published: March 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

Language: Английский

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A Healthcare System Employing Lightweight CNN for Disease Prediction with Artificial Intelligence

The Open Public Health Journal, Journal Year: 2024, Volume and Issue: 17(1)

Published: June 6, 2024

Introduction/Background This research introduces the EO-optimized Lightweight Automatic Modulation Classification Network (EO-LWAMCNet) model, employing AI and sensor data for forecasting chronic illnesses within Internet of Things framework. A transformative tool in remote healthcare monitoring, it exemplifies AI's potential to revolutionize patient experiences outcomes. study unveils a novel Healthcare System integrating Convolutional Neural (CNN) swift disease prediction through Artificial Intelligence. Leveraging efficiency lightweight CNN, model holds promise revolutionizing early diagnosis enhancing overall care. By merging advanced techniques, this improving Materials Methods The is implemented analyze real-time an (IoT) methodology also involves integration EO-LWAMCNet into cloud-based IoT ecosystem, demonstrating its reshaping monitoring expanding access high-quality care beyond conventional medical settings. Results Utilizing Chronic Liver Disease (CLD) Brain (BD) datasets, algorithm achieved remarkable accuracy rates 94.8% 95%, respectively, showcasing robustness as reliable clinical tool. Discussion These outcomes affirm model's reliability robust tool, particularly crucial diseases benefiting from detection. impact on emphasized suggesting paradigm shift traditional confines. Conclusion Our proposed presents cutting-edge solution with illnesses. revolutionization ecosystem underscores innovative

Language: Английский

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Optimizing Lung Condition Categorization through a Deep Learning Approach to Chest X-ray Image Analysis

BioMedInformatics, Journal Year: 2024, Volume and Issue: 4(3), P. 2002 - 2021

Published: Sept. 10, 2024

Background: Evaluating chest X-rays is a complex and high-demand task due to the intrinsic challenges associated with diagnosing wide range of pulmonary conditions. Therefore, advanced methodologies are required categorize multiple conditions from X-ray images accurately. Methods: This study introduces an optimized deep learning approach designed for multi-label categorization images, covering broad spectrum conditions, including lung opacity, normative states, COVID-19, bacterial pneumonia, viral tuberculosis. An model based on modified VGG16 architecture SE blocks was developed applied large dataset images. The evaluated against state-of-the-art techniques using metrics such as accuracy, F1-score, precision, recall, area under curve (AUC). Results: VGG16-SE demonstrated superior performance across all metrics. achieved accuracy 98.49%, F1-score 98.23%, precision 98.41%, recall 98.07% AUC 98.86%. Conclusion: provides effective categorizing X-rays. model’s high various suggests its potential integration into clinical workflows, enhancing speed disease diagnosis.

Language: Английский

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Enhancing Pulmonary Diagnosis in Chest X-rays through Generative AI Techniques

J — Multidisciplinary Scientific Journal, Journal Year: 2024, Volume and Issue: 7(3), P. 302 - 318

Published: Aug. 13, 2024

Chest X-ray imaging is an essential tool in the diagnostic procedure for pulmonary conditions, providing healthcare professionals with capability to immediately and accurately determine lung anomalies. This modality fundamental assessing confirming presence of various issues, allowing timely effective medical intervention. In response widespread prevalence infections globally, there a growing imperative adopt automated systems that leverage deep learning (DL) algorithms. These are particularly adept at handling large radiological datasets high precision. study introduces advanced identification model utilizes VGG16 architecture, specifically adapted identifying anomalies such as opacity, COVID-19 pneumonia, normal appearance lungs, viral pneumonia. Furthermore, we address issue generalizability, which prime significance our work. We employed data augmentation technique through CycleGAN, which, experimental outcomes, has proven enhancing robustness model. The combined performance VGG CycleGAN demonstrates remarkable outcomes several evaluation metrics, including recall, F1-score, accuracy, precision, area under curve (AUC). results showcased achieving 98.58%. contributes advancing generative artificial intelligence (AI) analysis establishes solid foundation ongoing developments computer vision technologies within sector.

Language: Английский

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From Binary to Multi-Class Classification: A Two-Step Hybrid CNN-ViT Model for Chest Disease Classification Based on X-Ray Images

Diagnostics, Journal Year: 2024, Volume and Issue: 14(23), P. 2754 - 2754

Published: Dec. 6, 2024

Background/Objectives: Chest disease identification for Tuberculosis and Pneumonia diseases presents diagnostic challenges due to overlapping radiographic features the limited availability of expert radiologists, especially in developing countries. The present study aims address these by a Computer-Aided Diagnosis (CAD) system provide consistent objective analyses chest X-ray images, thereby reducing potential human error. By leveraging complementary strengths convolutional neural networks (CNNs) vision transformers (ViTs), we propose hybrid model accurate detection distinguishing between Pneumonia. Methods: We designed two-step that integrates ResNet-50 CNN with ViT-b16 architecture. It uses transfer learning on datasets from Guangzhou Women’s Children’s Medical Center cases Qatar Dhaka (Bangladesh) universities cases. CNNs capture hierarchical structures while ViTs, their self-attention mechanisms, excel at identifying relationships features. Combining approaches enhances model’s performance binary multi-class classification tasks. Results: Our CNN-ViT achieved accuracy 98.97% detection. For classification, Tuberculosis, viral Pneumonia, bacterial an 96.18%. These results underscore improving reliability based images. Conclusions: proposed demonstrates substantial advancing robustness CAD systems diagnosis. integrating ViT architectures, our approach precision, which may help alleviate burden healthcare resource-limited settings improve patient outcomes

Language: Английский

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Attention-Driven Convolutional Neural Network for Accurate COVID-19 Diagnosis in Medical Imaging with GradCAM-Based Interpretability

2022 International Conference on Decision Aid Sciences and Applications (DASA), Journal Year: 2024, Volume and Issue: unknown, P. 1 - 7

Published: Dec. 11, 2024

Language: Английский

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Leveraging DenseNet Features for Machine Learning Based Lung Disease Diagnosis From X-Rays

Published: Nov. 14, 2024

Language: Английский

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