AI in Disease Detection : Advancements and Applications

• Autore: Singh Rajesh
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2025; ©2025
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (403 pages)
• Altri autori: GehlotAnita RathourNavjot Vaseem AkramShaik
• Nota di contenuto: Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- About the Editors -- List of Contributors -- Preface -- Acknowledgments -- Chapter 1 Introduction to AI in Disease Detection - An Overview of the Use of AI in Detecting Diseases, Including the Benefits and Limitations of the Technology -- Introduction -- Objectives -- Literature Review -- Benefits of AI in Disease Detection -- Limitations of AI in Disease Detection -- AI Techniques in Disease Detection -- Supervised Learning for Disease Diagnosis -- Unsupervised Learning in Healthcare -- Deep Learning and Convolutional Neural Networks (CNNs) -- AI in Medical Imaging and Radiology -- Applications of AI in Disease Detection -- Oncology: Cancer Detection and Diagnosis -- Cardiology: Predicting Cardiovascular Diseases -- Neurology: Early Detection of Neurological Disorders -- Infectious Diseases: AI in Epidemic and Pandemic Management -- Methodology -- Data Collection and Preprocessing -- Multimodal Fusion Techniques -- Transfer Learning for Disease Detection -- Explainable AI (XAI) Techniques -- Federated Learning Framework -- Clinical Validation and Adoption Studies -- Continuous Monitoring and Early Warning Systems -- Results and Analysis -- Analysis -- Performance Evaluation for the Techniques of Multimodal Fusion -- Assessment of Transfer Learning for Disease Detection -- Effectiveness of Explainable AI Techniques -- Privacy-Preserving Federated Learning-based Collaborative Model Training -- Performance of Continuous Monitoring and Early Warning Systems -- Case Study: AI in Disease Detection -- Development and Training -- Testing and Validation -- Deployment and Integration -- Conclusion -- Future Scope -- References -- Chapter 2 Explanation of Machine Learning Algorithms Used in Disease Detection, Such as Decision Trees and Neural Networks -- Introduction. ; The Silent Guardian: Machine Learning's Stealthy Rise in Disease Detection -- Beyond the Usual Suspects: A Look at Emerging Innovations -- The Ethical Symphony: Balancing Innovation with Human Oversight -- Objectives -- Unveiling Hidden Patterns - Feature Engineering -- Innovation Spotlight: Active Feature Acquisition (AFA) -- Limitations and Advantages of ML Algorithms for Disease Detection -- Advantages of Machine Learning Algorithms for Disease Detection -- Limitations of Machine Learning Algorithms for Disease Detection -- Literature Review -- The Familiar Melodies: Established ML Techniques and Their Strengths -- The Rise of the Deep Learning Chorus: Innovation on the Horizon -- Breaking New Ground: Unveiling Unique Innovations and Addressing Challenges -- The Well-Honed Orchestra: Established Techniques Take Center Stage -- Beyond the Familiar Melodies: Deep Learning Takes the Stage -- Collaboration and Innovation Lead the Way -- Methodology -- Conventional ML Methods for Disease Detection -- Beyond the Established Melodies: Innovation Takes Center Stage -- Results and Analysis -- The Familiar Melody: Established Methodologies -- The Disruptive Score: Unveiling New Innovations -- The Human Touch: Ethical Considerations and Explainability -- Conclusions and Future Scope -- The Evolving Maestro: AI Orchestration Beyond Established Methods -- Human-Machine Duet: Collaboration for a Healthier Future -- References -- Chapter 3 Natural Language Processing (NLP) in Disease Detection - A Discussion of How NLP Techniques Can Be Used to Analyze and Classify Medical Text Data for Disease Diagnosis -- Introduction -- Objectives -- Early Infection Location through Phonetic Fingerprints -- Estimation Examination for All-encompassing Healthcare -- Social Media Reconnaissance for Disease Outbreaks. ; Custom-fitted Medication through Personalized Content Investigation -- Precise Medication with Clinical Trial Content Mining -- Breaking Down Language Boundaries for Worldwide Wellbeing -- Human-Machine Collaboration for Making Strides -- Advantages and Limitations of Natural Language Processing in Disease Detection -- Advantages of NLP in Disease Detection -- Limitations of NLP in Disease Detection -- Literature Review -- From Content to Determination: Revealing Etymological Fingerprints -- Past Watchwords: Capturing the Subtlety of Free-Text Information -- Control of Expansive Language Models: A New Frontier -- Breaking Down Language Obstructions for Worldwide -- Toward a Collaborative Future: Human-Machine Association -- Logical AI -- Past Content: Multimodal Infection Discovery with NLP and Imaging Information -- Methodology -- Information Procurement and Preprocessing: Building the Establishment -- Content Explanation: Labeling the Story -- Feature Designing: Extricating Important Signals -- Show Determination and Preparing: Choosing the Right Tool for the Work -- Demonstrate Assessment and Refinement: Guaranteeing Exactness and Belief -- Integration and Arrangement: Putting NLP to Work -- Results and Analysis -- Current Achievements: A Glimpse into the Possible -- Unveiling New Frontiers: Innovative Approaches for the Future -- Challenges and Considerations: Navigating the Road Ahead -- Case Study of NLP in Disease Detection -- Conclusions and Future Scope -- Charting the Course: Unveiling New Frontiers in NLP -- A Collaborative Future: Working Together for a Healthier Tomorrow -- Enhancing EHR Analysis -- Personalized Pharmaceutical -- Integration with AI and Machine Learning -- Expansion into New Medical Fields -- Upgrading Persistent Engagement -- Ethical and Protection Contemplations -- References. ; Chapter 4 Computer Vision for Disease Detection - An Overview of How Computer Vision Techniques Can Be Used to Detect Diseases in Medical Images, Such as X-rays and MRIs -- Introduction -- Objectives -- Improved Early Disease Detection -- Improve Diagnostic Accuracy -- Developing Transfer Learning Models for Medical Imaging -- Explainability in Artificial Intelligence Applied to Medical Imaging -- Building Computer-Vision-Based Real-Time Disease Diagnostics Systems -- Integration of Multimodal Data for Comprehensive Diagnosis -- Literature Review -- Improving Early Detection and Diagnostic Accuracy -- Switch Studying and Artificial Records Generation -- Explainable AI and Real-Time Detection Structures -- Multimodal Statistics Integration -- Innovations in Precise Disease Detection -- Advanced Deep Learning Strategies -- Statistics Augmentation and Synthesis -- Explainable AI for Trust and Transparency -- Real-Time Diagnostic Systems -- Integration of Multimodal Insights -- Disease-specific Innovations -- Benefits of AI in Disease Detection -- Limitations of AI in Disease Detection -- Methodology -- Records Series and Preprocessing -- Version Improvement -- Real-Time Processing and Deployment -- Multimodal Records Integration -- Continuous Mastering and Development -- Results and Analysis -- Diagnostic Accuracy -- Efficiency and Pace -- Explainability and Agreement -- Multimodal Statistics Integration -- Key Improvements -- Continuous Learning and Variation -- Medical Integration and Impact -- Key Improvements -- Conclusion and Future Scope -- References -- Chapter 5 Deep Learning for Disease Detection - A Deep Dive into Deep Learning Techniques Such as Convolutional Neural Networks (CNNs) and Their Use in Disease Detection -- Introduction -- Objectives -- Literature Review -- Integration of Multimodal Information. ; Switch Learning for Better Model Training -- Explainable AI Techniques for CNNs -- Records Augmentation and Synthesis Techniques -- Fundamentals of Deep Learning -- CNNs in Medical Imaging -- Image Processing for Disease Detection -- Methodology -- Convolutional Neural Networks: A Top-level View -- Multiscale Convolutional Layers -- Attention Mechanisms -- Transfer Learning with Pretrained Models -- Generative Adversarial Networks (GANs) for Statistics Augmentation -- Self-Supervised Learning -- Results and Analysis -- Accuracy and Performance -- Enhanced Diagnostic Accuracy -- Sensitivity and Specificity -- Speed and Efficiency -- Reliability and Consistency -- Effects -- Multiscale Convolutional Layers -- Attention Mechanisms -- Switch Learning with Pretrained Models -- GANs for Statistics Augmentation -- Self-SupervisedLearning -- Improved Diagnostic Accuracy and Performance -- Reduced Dependence on Massive Labeled Datasets -- Better Version Robustness and Generalization -- Scalability and Flexibility -- Innovations and Future Instructions -- Multimodal Gaining Knowledge -- Federated Learning for Privateness-RetainingAI -- Explainable AI (XAI) for Stepped Forward Interpretability -- Integration with Wearable Devices -- Real-TimeAdaptive Learning -- Conclusion and Future Scope -- Multimodal Deep Learning Integration -- Federated Learning for Stronger Privacy -- Explainable AI (XAI) for Transparency -- Wearable Generation AI and Continuous Monitoring -- Adaptive Learning and Real-Time Model Updating -- Personalized Remedy and Predictive Analytics -- Collaborative AI Systems -- Stronger Data Augmentation Techniques -- AI-driven Clinical Trials and Research -- International Health and AI-driven Disorder Surveillance -- References. ; Chapter 6 Applications of AI in Cardiovascular Disease Detection - A Review of the Specific Ways in which AI Is Being Used to Detect and Diagnose Cardiovascular Diseases.
• Titolo autorizzato: AI in Disease Detection
• ISBN: 9781394278671; 1394278675; 9781394278688; 1394278683
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910920924803321
• Lo trovi qui: Univ. Federico II