Human Cancer Diagnosis and Detection Using Exascale Computing

• Autore: Joshi Kapil
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (330 pages)
• Disciplina: 616.9940750285
• Soggetto topico: Exascale computing; Cancer - Diagnosis
• Altri autori: GuptaSomil Kumar
• Nota di contenuto: Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Evaluating the Impact of Healthcare 4.0 on the Performance of Hospitals -- 1.1 Introduction -- 1.2 Literature Review -- 1.3 Methodology -- 1.3.1 Selection of the Sample and Characterization -- 1.3.2 Creation of a Data-Gathering Tool and Measures -- 1.3.3 Inspection of the Conceptions' Reliability and Validity -- 1.3.4 Data Evaluation -- 1.4 Result and Discussion -- 1.5 Conclusion -- References -- Chapter 2 Human Breast Cancer Classification Employing the Machine Learning Ensemble -- 2.1 Introduction -- 2.1.1 Breast Cancer Symptoms and Signs -- 2.1.2 Breast Cancer Risk Factors -- 2.1.3 Disease Prediction Using Machine Learning -- 2.2 Literature Review -- 2.3 Methodology -- 2.3.1 Bayesian Network -- 2.3.2 Radial Basis Function -- 2.3.3 Ensemble Learning -- 2.3.4 The Suggested Algorithm -- 2.4 Results and Discussion -- 2.5 Conclusion -- References -- Chapter 3 Multi-Objective Differential Development Using DNN for Multimodality Medical Image Fusion -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Methodology -- 3.3.1 Non-Subsampled Contourlet Transform -- 3.3.2 Deep Xception Mode Feature Extraction -- 3.3.3 Differential Evolutions with Several Objectives for Feature Selection -- 3.3.4 Fusion of High-Frequency Bands -- 3.4 Result and Discussion -- 3.4.1 Visual Evaluation -- 3.4.2 Quantitative Research -- 3.5 Conclusion -- References -- Chapter 4 Multimodal Deep Learning Analysis for Biomedical Data Fusion -- 4.1 Introduction -- 4.2 Literature Review -- 4.3 Methodology -- 4.3.1 Early Fusion -- 4.3.2 Intermediate Fusion -- 4.3.3 Late Fusion -- 4.4 Results and Discussion -- 4.5 Conclusion -- References -- Chapter 5 Developing Robot-Based Neurorehabilitation Exercises Using a Teaching-Training Process -- 5.1 Introduction -- 5.1.1 Research Gap -- 5.1.2 Research Aim. ; 5.2 Literature Review -- 5.3 Research Methodology -- 5.4 Results -- 5.5 Conclusion -- 5.6 Future Research Directions -- References -- Chapter 6 Investigation on Introduction to Heterogeneous Exascale Computing in the Medical Field -- 6.1 Introduction -- 6.1.1 Research Gap -- 6.2 Literature Review -- 6.3 Research Methodology -- 6.4 Results and Discussion -- 6.5 Conclusion -- 6.6 Future Research Direction -- References -- Chapter 7 Adoption of Cloud Computing in the Healthcare Field Using the SEM Approach -- 7.1 Introduction -- 7.1.1 Research Gap -- 7.1.2 Research Aim -- 7.2 Literature Review -- 7.3 Research Methodology -- 7.3.1 Research Hypothesis -- 7.3.2 Data Analysis -- 7.4 Results and Discussion -- 7.5 Implications -- 7.6 Conclusion -- 7.7 Future Research Directions -- References -- Chapter 8 Chest X-Ray Analysis for COVID-19 Diagnosis Using an Exascale Computation and Machine Learning Framework -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Research Methodology -- 8.4 Analysis and Discussion -- 8.5 Conclusion -- References -- Chapter 9 3D-Printed Human Organ Designs with Tissue Physical Characteristics and Embedded Sensors -- 9.1 Introduction -- 9.2 Literature Review -- 9.3 Methodology -- 9.4 Analysis and Discussion -- 9.5 Conclusion -- References -- Chapter 10 Fast Computing Network Infrastructure for Healthcare Systems Based on 6G Future Perspective -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Research Methodology -- 10.4 Analysis and Discussion -- 10.5 Conclusion -- References -- Chapter 11 Analysis of Multimodality Fusion of Medical Image Segmentation Employing Deep Learning -- 11.1 Introduction -- 11.1.1 Research Gap -- 11.1.2 Research Aim -- 11.2 Literature Review -- 11.3 Research Methodology -- 11.4 Results and Discussion -- 11.5 Conclusion -- References. ; Chapter 12 New Perspectives, Challenges, and Advances in Data Fusion in Neuroimaging -- 12.1 Introduction -- 12.1.1 Research Gap -- 12.2 Literature Review -- 12.3 Research Methodology -- 12.3.1 Human Brain Temporal and Spatial Data Mining Using FOCA and Data Fusion -- 12.3.2 Construction of the Multimodal Neuroimaging Data Fusion -- 12.4 Results and Discussion -- 12.4.1 EEG-fMRI Shared Multimodal Simulation Evaluation -- 12.4.2 Implementation of Multimodal Neuroimaging Data Fusion -- 12.5 Challenges -- 12.6 Conclusion -- References -- Chapter 13 The Potential of Cloud Computing in Medical Big Data Processing Systems -- 13.1 Introduction -- 13.2 Literature Review -- 13.3 Materials and Method -- 13.4 Result and Discussion -- 13.5 Conclusion -- References -- Chapter 14 Deep Learning (DL) on Exascale Computing to Speed Up Cancer Investigation -- 14.1 Introduction -- 14.2 Literature Review -- 14.3 Research Methodology -- 14.4 Analysis and Discussion -- 14.5 Conclusion -- References -- Chapter 15 Current Breakthroughs and Future Perspectives in Surgery Based on AI-Based Computing Vision -- 15.1 Introduction -- 15.2 Literature Review -- 15.3 Research Methodology -- 15.4 Analysis and Discussion -- 15.5 Conclusion -- References -- Chapter 16 MRI-Based Brain Tumor Detection Using Machine Learning -- 16.1 Introduction -- 16.2 Pre-Processing -- 16.3 Segmentation -- 16.4 Feature Extraction -- 16.5 SVM Classifier -- 16.6 Methodology -- 16.7 Conclusion -- References -- Chapter 17 Chili Pepper as a Natural Therapeutic Drug: A Review of Its Anticancer and Antioxidant Properties and Mechanism of Action Using the Machine Learning Approach -- 17.1 Introduction -- 17.2 Machine Learning Technique -- 17.3 Composition Profile -- 17.4 Reactions of Phytochemicals to Drying and Ripening -- 17.5 Antioxidant Activity -- 17.6 Anticancer Activity. ; 17.7 Activities that are Anti-Inflammatory and Relieve Pain -- 17.8 Activities Controlling Diabetes and Hyperglycemia -- 17.9 The Impacts of Anticholesteremic Activity on Lipid Metabolism -- 17.10 Anticlotting Effect -- 17.11 Antimicrobial Activity -- 17.12 Immune Checkpoint Signaling -- 17.13 Suppression of Antitumor Immune Response -- 17.14 Antigen Masking -- 17.15 Immune-Based Cancer Therapies -- 17.16 Other Miscellaneous Medicinal Values -- 17.17 Conclusion -- References -- Chapter 18 Exascale Computing: The Next Frontier of High-Performance Computing -- 18.1 Introduction -- 18.1.1 Literature Study -- 18.2 Exascale Computing -- 18.2.1 Exascale Computers -- 18.2.2 Case Study -- 18.2.3 Measuring Computer Speed -- 18.2.4 Usage of FLOPS in Supercomputers -- 18.2.5 Exascale Computing: A Crucial Technology -- 18.2.6 Requirements of High-Speed Computers -- 18.2.7 Milestones -- 18.2.8 Exascale Computing Processing -- 18.2.9 Advantages of Exascale Computing -- 18.2.10 Exascale Computing in Various Domains -- 18.2.11 Exascale Computer: A Supercomputer -- 18.2.12 Exascale Computing Different from Quantum Computing -- 18.3 Exascale Computing Challenges -- 18.4 Future Lookup -- 18.4.1 Needed Improvements -- 18.5 Conclusion -- References -- Index -- EULA.
• Sommario/riassunto: This comprehensive volume explores the utilization of exascale computing in the diagnosis and detection of human cancers. It delves into the application of advanced computing technologies, such as machine learning and deep learning, for medical imaging, data fusion, and the treatment of cancer. The book covers various topics including multimodal data analysis, cloud computing in healthcare, and the development of fast computing networks for medical systems. It aims to provide insights into the integration of computational methods with healthcare practices to enhance cancer diagnosis and treatment. The book is intended for researchers, practitioners, and academics in fields of computer science, biomedical engineering, and healthcare technology.
• Titolo autorizzato: Human Cancer Diagnosis and Detection Using Exascale Computing
• ISBN: 9781394197705; 1394197705; 9781394197699; 1394197691
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911020320203321
• Lo trovi qui: Univ. Federico II