Info
AI in Disease Detection : Advancements and Applications
| AI in Disease Detection : Advancements and Applications |
| Autore | Singh Rajesh |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (403 pages) |
| Disciplina | 616.07/50285 |
| Altri autori (Persone) |
GehlotAnita
RathourNavjot Vaseem AkramShaik |
| Soggetto topico |
Diagnosis - Data processing
Artificial intelligence - Medical applications Diagnosis - Technological innovations |
| ISBN |
9781394278695
1394278691 9781394278671 1394278675 9781394278688 1394278683 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNINA-9911018804203321 |
Singh Rajesh
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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BIYOMUT : 2017 21st National Biomedical Engineering Meeting : 4 November-26 December 2017, Istanbul, Turkey / / Institute of Electrical and Electronics Engineers
| BIYOMUT : 2017 21st National Biomedical Engineering Meeting : 4 November-26 December 2017, Istanbul, Turkey / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 |
| Descrizione fisica | 1 online resource (855 pages) |
| Disciplina | 610.28 |
| Soggetto topico |
Biomedical engineering
Diagnosis - Data processing |
| ISBN | 1-5386-5340-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996279921203316 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
BIYOMUT : 2017 21st National Biomedical Engineering Meeting : 4 November-26 December 2017, Istanbul, Turkey / / Institute of Electrical and Electronics Engineers
| BIYOMUT : 2017 21st National Biomedical Engineering Meeting : 4 November-26 December 2017, Istanbul, Turkey / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 |
| Descrizione fisica | 1 online resource (855 pages) |
| Disciplina | 610.28 |
| Soggetto topico |
Biomedical engineering
Diagnosis - Data processing |
| ISBN | 1-5386-5340-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910287960303321 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Compression of biomedical images and signals [[electronic resource] /] / edited by Amine Nait-Ali, Christine Cavaro-Menard
| Compression of biomedical images and signals [[electronic resource] /] / edited by Amine Nait-Ali, Christine Cavaro-Menard |
| Autore | Naït-Ali Amine |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (330 p.) |
| Disciplina |
616.07/50285
616.0750285 |
| Altri autori (Persone) |
Naït-AliAmine
Cavaro-MénardChristine |
| Collana | ISTE |
| Soggetto topico |
Diagnosis - Data processing
Data compression (Computer science) Medical informatics |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-16503-8
9786612165030 0-470-61115-4 0-470-39378-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Compression of Biomedical Images and Signals; Table of Contents; Preface; Chapter 1. Relevance of Biomedical Data Compression; 1.1. Introduction; 1.2. The management of digital data using PACS; 1.2.1. Usefulness of PACS; 1.2.2. The limitations of installing a PACS; 1.3. The increasing quantities of digital data; 1.3.1. An example from radiology; 1.3.2. An example from anatomic pathology; 1.3.3. An example from cardiology with ECG; 1.3.4. Increases in the number of explorative examinations; 1.4. Legal and practical matters; 1.5. The role of data compression; 1.6. Diagnostic quality
1.6.1. Evaluation1.6.2. Reticence; 1.7. Conclusion; 1.8. Bibliography; Chapter 2. State of the Art of Compression Methods; 2.1. Introduction; 2.2. Outline of a generic compression technique; 2.2.1. Reducing redundancy; 2.2.2. Quantizing the decorrelated information; 2.2.3. Coding the quantized values; 2.2.4. Compression ratio, quality evaluation; 2.3. Compression of still images; 2.3.1. JPEG standard; 2.3.1.1. Why use DCT?; 2.3.1.2. Quantization; 2.3.1.3. Coding; 2.3.1.4. Compression of still color images with JPEG; 2.3.1.5. JPEG standard: conclusion; 2.3.2. JPEG 2000 standard 2.3.2.1. Wavelet transform2.3.2.2. Decomposition of images with the wavelet transform; 2.3.2.3. Quantization and coding of subbands; 2.3.2.4. Wavelet-based compression methods, serving as references; 2.3.2.5. JPEG 2000 standard; 2.4. The compression of image sequences; 2.4.1. DCT-based video compression scheme; 2.4.2. A history of and comparison between video standards; 2.4.3. Recent developments in video compression; 2.5. Compressing 1D signals; 2.6. The compression of 3D objects; 2.7. Conclusion and future developments; 2.8. Bibliography Chapter 3. Specificities of Physiological Signals and Medical Images3.1. Introduction; 3.2. Characteristics of physiological signals; 3.2.1. Main physiological signals; 3.2.1.1. Electroencephalogram (EEG); 3.2.1.2. Evoked potential (EP); 3.2.1.3. Electromyogram (EMG); 3.2.1.4. Electrocardiogram (ECG); 3.2.2. Physiological signal acquisition; 3.2.3. Properties of physiological signals; 3.2.3.1. Properties of EEG signals; 3.2.3.2. Properties of ECG signals; 3.3. Specificities of medical images; 3.3.1. The different features of medical imaging formation processes; 3.3.1.1. Radiology 3.3.1.2. Magnetic resonance imaging (MRI)3.3.1.3. Ultrasound; 3.3.1.4. Nuclear medicine; 3.3.1.5. Anatomopathological imaging; 3.3.1.6. Conclusion; 3.3.2. Properties of medical images; 3.3.2.1. The size of images; 3.3.2.2. Spatial and temporal resolution; 3.3.2.3. Noise in medical images; 3.4. Conclusion; 3.5. Bibliography; Chapter 4. Standards in Medical Image Compression; 4.1. Introduction; 4.2. Standards for communicating medical data; 4.2.1. Who creates the standards, and how?; 4.2.2. Standards in the healthcare sector; 4.2.2.1. Technical committee 251 of CEN 4.2.2.2. Technical committee 215 of the ISO |
| Record Nr. | UNINA-9910139467303321 |
|
Naït-Ali Amine
|
||
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Compression of biomedical images and signals / / edited by Amine Nait-Ali, Christine Cavaro-Menard
| Compression of biomedical images and signals / / edited by Amine Nait-Ali, Christine Cavaro-Menard |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (330 p.) |
| Disciplina | 616.07/50285 |
| Altri autori (Persone) |
Nait-AliAmine
Cavaro-MenardChristine |
| Collana | ISTE |
| Soggetto topico |
Diagnosis - Data processing
Data compression (Computer science) Medical informatics |
| ISBN |
9786612165030
9781282165038 1282165038 9780470611159 0470611154 9780470393789 0470393785 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Compression of Biomedical Images and Signals; Table of Contents; Preface; Chapter 1. Relevance of Biomedical Data Compression; 1.1. Introduction; 1.2. The management of digital data using PACS; 1.2.1. Usefulness of PACS; 1.2.2. The limitations of installing a PACS; 1.3. The increasing quantities of digital data; 1.3.1. An example from radiology; 1.3.2. An example from anatomic pathology; 1.3.3. An example from cardiology with ECG; 1.3.4. Increases in the number of explorative examinations; 1.4. Legal and practical matters; 1.5. The role of data compression; 1.6. Diagnostic quality
1.6.1. Evaluation1.6.2. Reticence; 1.7. Conclusion; 1.8. Bibliography; Chapter 2. State of the Art of Compression Methods; 2.1. Introduction; 2.2. Outline of a generic compression technique; 2.2.1. Reducing redundancy; 2.2.2. Quantizing the decorrelated information; 2.2.3. Coding the quantized values; 2.2.4. Compression ratio, quality evaluation; 2.3. Compression of still images; 2.3.1. JPEG standard; 2.3.1.1. Why use DCT?; 2.3.1.2. Quantization; 2.3.1.3. Coding; 2.3.1.4. Compression of still color images with JPEG; 2.3.1.5. JPEG standard: conclusion; 2.3.2. JPEG 2000 standard 2.3.2.1. Wavelet transform2.3.2.2. Decomposition of images with the wavelet transform; 2.3.2.3. Quantization and coding of subbands; 2.3.2.4. Wavelet-based compression methods, serving as references; 2.3.2.5. JPEG 2000 standard; 2.4. The compression of image sequences; 2.4.1. DCT-based video compression scheme; 2.4.2. A history of and comparison between video standards; 2.4.3. Recent developments in video compression; 2.5. Compressing 1D signals; 2.6. The compression of 3D objects; 2.7. Conclusion and future developments; 2.8. Bibliography Chapter 3. Specificities of Physiological Signals and Medical Images3.1. Introduction; 3.2. Characteristics of physiological signals; 3.2.1. Main physiological signals; 3.2.1.1. Electroencephalogram (EEG); 3.2.1.2. Evoked potential (EP); 3.2.1.3. Electromyogram (EMG); 3.2.1.4. Electrocardiogram (ECG); 3.2.2. Physiological signal acquisition; 3.2.3. Properties of physiological signals; 3.2.3.1. Properties of EEG signals; 3.2.3.2. Properties of ECG signals; 3.3. Specificities of medical images; 3.3.1. The different features of medical imaging formation processes; 3.3.1.1. Radiology 3.3.1.2. Magnetic resonance imaging (MRI)3.3.1.3. Ultrasound; 3.3.1.4. Nuclear medicine; 3.3.1.5. Anatomopathological imaging; 3.3.1.6. Conclusion; 3.3.2. Properties of medical images; 3.3.2.1. The size of images; 3.3.2.2. Spatial and temporal resolution; 3.3.2.3. Noise in medical images; 3.4. Conclusion; 3.5. Bibliography; Chapter 4. Standards in Medical Image Compression; 4.1. Introduction; 4.2. Standards for communicating medical data; 4.2.1. Who creates the standards, and how?; 4.2.2. Standards in the healthcare sector; 4.2.2.1. Technical committee 251 of CEN 4.2.2.2. Technical committee 215 of the ISO |
| Record Nr. | UNINA-9910677012803321 |
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computers in Cardiology
| Computers in Cardiology |
| Pubbl/distr/stampa | [Place of publication not identified], : I E E E, 2001 |
| Descrizione fisica | 1 online resource (700 pages) : illustrations |
| Disciplina | 616.12 |
| Soggetto topico |
Cardiology - Data processing
Diagnosis - Data processing Patient monitoring - Data processing |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996207367903316 |
| [Place of publication not identified], : I E E E, 2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Computers in Cardiology
| Computers in Cardiology |
| Pubbl/distr/stampa | [Place of publication not identified], : I E E E, 2001 |
| Descrizione fisica | 1 online resource (700 pages) : illustrations |
| Disciplina | 616.12 |
| Soggetto topico |
Cardiology - Data processing
Diagnosis - Data processing Patient monitoring - Data processing |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910872487103321 |
| [Place of publication not identified], : I E E E, 2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Digital diagnostics
| Digital diagnostics |
| Pubbl/distr/stampa | Sankt-Peterburg : , : OOO "Ėko-Vektor", , [2020]- |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Diagnosis
Diagnosis - Data processing Diagnostics Diagnostics - Informatique |
| Soggetto genere / forma | Periodicals. |
| ISSN | 2712-8962 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | rus |
| Record Nr. | UNISA-996426343703316 |
| Sankt-Peterburg : , : OOO "Ėko-Vektor", , [2020]- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Digital diagnostics
| Digital diagnostics |
| Pubbl/distr/stampa | Sankt-Peterburg : , : OOO "Ėko-Vektor", , [2020]- |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Diagnosis
Diagnosis - Data processing Diagnostics Diagnostics - Informatique |
| Soggetto genere / forma | Periodicals. |
| ISSN | 2712-8962 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | rus |
| Record Nr. | UNINA-9910487548403321 |
| Sankt-Peterburg : , : OOO "Ėko-Vektor", , [2020]- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The Internet journal of medical simulation
| The Internet journal of medical simulation |
| Pubbl/distr/stampa | [Sugar Land, Tex.], : Internet Scientific Publications, [2003]- |
| Descrizione fisica | 1 online resource |
| Disciplina | 610 |
| Soggetto topico |
Diagnosis - Data processing
Clinical medicine - Computer programs Computer simulation Diagnosis, Computer-Assisted Computer Simulation Software Therapy, Computer-Assisted |
| Soggetto genere / forma |
Periodical
Periodicals. |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Journal of medical simulation
Medical simulation |
| Record Nr. | UNINA-9910146420903321 |
| [Sugar Land, Tex.], : Internet Scientific Publications, [2003]- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||