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Covid-19 Radiological Lung Imaging de Luca Saba

Covid-19 Radiological Lung Imaging

Editat de Luca Saba, Sushant Agarwal, Jasjit S Suri
en Limba Engleză Paperback – 3 noi 2025
COVID-19 Radiological Lung Imaging: A Classic Artificial Intelligence Framework introduces modern AI technologies for early detection of COVID-19 disease to assist in saving patient lives and safeguarding frontline workers. With a strong focus on Deep Learning, the book examines specific detection and classification techniques in lung X-ray imaging, computed tomography lung imaging, deep learning on edge devises and bias measurements, deep learning for cloud and explainable AI for validation, and discusses the medical impact and AI implications for COVID-19 in lung pathologies. It is therefore an ideal reference for researchers and clinicians working in radiology and pulmonary medicine to learn the modern AI technologies in COVID-19 paradigms for implementation.


  • Offers broad and complete coverage in AI in healthcare regarding detection, classification, explainable AI, cloud-based diagnosis, pruning, and bias technologies in radiology
  • Reviews AI systems technology that can be incorporated into medical devices as well as in many diagnoses and treatment procedures
  • Contributes to early detection techniques of COVID-19 disease through AI technologies
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Specificații

ISBN-13: 9780443138744
ISBN-10: 0443138745
Pagini: 450
Dimensiuni: 191 x 235 mm
Editura: ELSEVIER SCIENCE

Cuprins

Section 1: X-Ray Lung Imaging using Deep Learning
1. Lung Segmentation using Lung X-ray Scans: U-Series
2. Lung Classification using Lung X-ray Scans
3. Heatmap using Explainable AI on Lung X-ray Scans
4. Lesion Segmentation using Lung X-ray Scans: Hybrid U-Series

Section 2: Computed Tomography Lung Imaging using Solo and Hybrid Deep Learning
5. Deep Learning-Based Characterization of Acute Respiratory Distress Syndrome in COVID-19-Infected Lungs
6. Hybrid Deep Learning Artificial Intelligence Models for Lung Segmentation in COVID-19 Computed Tomography Scans
7. Hybrid Deep Learning Models based on COVID-19 Lung Segmentation in Computed Tomography using Inter-Variability Framework
8. Hybrid Deep Learning in a Multicenter Framework for Automated COVID-19 Lung Segmentation

Section 3: Pruning & Optimization Deep Learning Techniques for Computed Tomography COVID-19 Imaging
9. Lesion Segmentation in COVID-19 Lung using Artificial Intelligence Framework for Automated Computed Tomography Scans
10. Artificial Intelligence-Based External Validation Framework for Computed Tomography Lung Segmentation using Italian and Croatian Cohorts
11. Pruning of COVID-19 Computed Tomography based Lung Segmentation Deep Learning Models for Storage and Performance Improvement and its Validation using Class Activation Map Techniques

Section 4: Deep Learning on Edge Devices for COVID-19 & Bias Measurements in Deep Learning
12. Deep Learning for COVID-19 deployment on Low-Cost Edge Device: Raspberry Pie
13. Systematic Review of Artificial Intelligence Based Paradigm in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients
14. Five Strategies for Bias Estimation in Hybrid Deep Learning for Acute Respiratory Distress Syndrome COVID-19 Lung Infected Patients

Section 5: Deep Learning on Cloud for COVID-19 and Explainable AI for Validation
15. Deep Learning deployment on Cloud for COVID-19 Lung Segmentation
16. Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans in a Cloud Environment

Section 6: Medical Impact and AI Application for COVID-19 in Lung Pathologies
17. Lung COVID from pathology to radiological features
18. Lung COVID and pulmonary embolism
19. Classification systems in X-ray for Lung pathology COVID based
20. Classification systems in CT for Lung pathology COVID based
21. A changing landscape: integration of AI models that incorporate lung imaging data and biological, molecular for the model of risk prediction.