Liebe Besucherinnen und Besucher,
aufgrund unseres Sommerfestes sind wir am 03. September 2026 bis 14 Uhr erreichbar. Am 04. September 2026 sind wir wieder wie gewohnt für Sie da. Vielen Dank für Ihr Verständnis.
Ihr Team von Sack Fachmedien
Buch, Englisch, Format (B × H): 191 mm x 234 mm
Methods, Algorithms and Applications
Buch, Englisch, Format (B × H): 191 mm x 234 mm
ISBN: 978-0-443-13310-7
Verlag: Elsevier Science & Technology
When diagnosing critical disease a timely and accurate detection of the problems or symptoms experienced by the patient is critical. Machine and deep learning methods provide the technology to create quicker and more accurate diagnoses by automating the detection process.This book presents advanced machine and deep learning methods for automating the diagnosis of critical disease. It provides the methods and algorithms for analyzing complex images to diagnose disease. The types of diseases it covers are coronary artery, cancer, tumours, lung and kidney.This book is a comprehensive resource for engineers or computer scientists looking to apply machine and deep learning methods to image analysis for the purpose of diagnosing disease.
Autoren/Hrsg.
Fachgebiete
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
- Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
Weitere Infos & Material
Broad Table of Content of Book:
1. Role of Artificial intelligence and machine learning in medical diagnosis.
Tentative Chapters and Authors'
Chapter 1: Challenges for implementing the artificial intelligence and machine learning in the diagnosis of diseases.
Kalpana Chauhan, Central University of Haryana Mahrndragarh, India, Rajeev Kumar Chauhan
Chapter 2: Effective techniques for particular diagnosis
Milad Mirbabaie, Faculty of Business Administration and Economics, Paderborn University, Paderborn, Germany, Stefan Stieglitz, University of Duisburg-Essen, Professional Communication in Electronic Media/Social Media, Duisburg, Germany
2. Machine Learning based diagnosis techniques
Chapter 3: Artificial intelligent technique for automated diagnosis of coronary artery disease
Francisco Lopez-Jimenez,Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, Carlos Martin-Isla, Departament de Matem�tiques & Inform�tica, Universitat de Barcelona, Barcelona, Spain
Chapter 4: Artificial intelligent technique for automated diagnosis of lung infection
Xueyan Mei, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Hao-Chih Lee, Kai-yue Diao.
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Chapter 5: Artificial intelligent technique for automated diagnosis of tumors
Wenya Linda Bi, Department of Neurosurgery, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, Zodwa Dlamini, Pan African Cancer Research Institute (PACRI), University of Pretoria, Faculty of Health Sciences, Hatfield 0028, South Africa.
3. Deep Learning based diagnosis techniques
Chapter 6: Supervised machine learning methods for diagnosis and severity identification of diseases
Juan A. Gomez-Pulido, Universidad de Extremadura, Department of Computers and Communications Technology.
Chapter 7: Unsupervised learning methods for diagnosis and severity identification of diseases.
Alexander Selvikv�g Lundervold, Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Norway.
4. Neural Network and Fuzzy Algorithms
Chapter 8: Neuro Network models for diagnosis of medical diseases.
Zhao Pei, University of Alberta
Carlos Enrique Montenegro Marin, Universidad Distrital Francisco Jos� de
Chapter 9: Fuzzy models for diagnosis of medical diseases.
Patricia Melin, Tijuana Institute of Technology
Holida Primova,Samarkand Branch of Tashkent University of Information Technologies
Chapter 10: Neuro-Fuzzy hybrid models for diagnosis of medical diseases
Tianhua Chen,University of Huddersfield, UK
Celestine Iwendi, University of Bolton, UK
5. Case Studies on various modalities
Chapter 11: Progressive analysis of cancer or lung disease
Simon Walsh, National Heart and Lung Institute, Imperial College, London
Robert Haddad, M.D., Dana Farber Cancer Institute, Harvard Medical School
Chapter 12: sepsis and septic shock prediction using machine learning models
6. Future advancement and challenges with machine learning
Chapter 13: Integration of AI and IoT for the prediction and analysis of diseases records.
Youn-Hee Han, Computer Science and Engineering, Korea University of Technology and Education
Chapter 14: Prediction of severity growth of cancerous diseases.
Andr� F Rendeiro, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell
Chapter 15: Real-time AI models for medical image processing
Murray Loew, George Washington University
Chapter 16: Medical issues and adaptation of machine learning and deep learning in clinical diagnosis
Jonathan G. Richens, Babylon Health, 60 Sloane Ave, Chelsea, London, SW3 3DD, UK
Chapter 17: Machine learning explainability in medical applications
Chapter 18. Interpretability of machine learning-based prediction models for various diseases.
