Machine and Deep Learning for Automated Diagnosis of Critical diseases
Methods, Algorithms and Applications
Will be published approx. on 2. January 2029
Book
Paperback/Softback
978-0-443-13310-7 (ISBN)
Description
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.
More details
Persons
Editor
Assistant Professor, Department of Electrical Engineering, Central University of Haryana, Mahendargarh, India
Dr. Kalpana Chauhan (M'13-SM'17) ) graduated in Electrical and Electronics Engineering from The College of Engineering Roorkee, India. She received her M. Tech degree in Control and Instrumentation Engineering from Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India and PhD in Electrical Engineering from the Indian Institute of Technology Mandi, India. Presently She is working as Associate Professor at the Galgotias College of Engineering and Technology Greater Noida. Dr. Chauhan worked as Dean (Research) at SIRDA Group of Institutions Sundernagar, India (2013-2017). She has also worked as Visiting Scientist in the Center for Electromechnics at the University of Texas at Austin, US. Her special field of interest included DC Micro-grid, Building Automation System, Industrial Automation and Control. She is also the associate member of Institution of Engineers (India), IAENG Hong Kong and ICASIT Singapore.
Dr. Kalpana Chauhan (M'13-SM'17) ) graduated in Electrical and Electronics Engineering from The College of Engineering Roorkee, India. She received her M. Tech degree in Control and Instrumentation Engineering from Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India and PhD in Electrical Engineering from the Indian Institute of Technology Mandi, India. Presently She is working as Associate Professor at the Galgotias College of Engineering and Technology Greater Noida. Dr. Chauhan worked as Dean (Research) at SIRDA Group of Institutions Sundernagar, India (2013-2017). She has also worked as Visiting Scientist in the Center for Electromechnics at the University of Texas at Austin, US. Her special field of interest included DC Micro-grid, Building Automation System, Industrial Automation and Control. She is also the associate member of Institution of Engineers (India), IAENG Hong Kong and ICASIT Singapore.
Assistant Professor, Department of Electrical Engineering, Dayalbagh Educational Institute, Agra, India
Rajeev Kumar Chauhan (M'10-SM'15) graduated in Electrical Engineering from The Institutions of Engineers (India).He received his M. Tech degree in Control and Instrumentation Engineering from Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India and PhD from School of Computing and Electrical Engineering at Indian Institute of Technology Mandi, India. Presently he is working as Assistant Professor (Senior Grade) at the Galgotias College of Engineering and Technology Greater Noida, India. Dr. Chauhan received POSOCO Power system Award (PPSA-2017) and Best PhD Thesis Award to recognize and reward innovative technical research excellence in power system. He has also received 2nd prize (Category Energy) in IEEE IAS CMD Humanitarian project contest 2017 for his project DC Micro-grid for Residential Buildings. He also received Bhaskara Advanced Solar Energy (BASE-2014) Award, for his research proposal in the area of DC microgrid from Department of Science and Technology, Indo-US Science and Technology Forum.
Rajeev Kumar Chauhan (M'10-SM'15) graduated in Electrical Engineering from The Institutions of Engineers (India).He received his M. Tech degree in Control and Instrumentation Engineering from Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India and PhD from School of Computing and Electrical Engineering at Indian Institute of Technology Mandi, India. Presently he is working as Assistant Professor (Senior Grade) at the Galgotias College of Engineering and Technology Greater Noida, India. Dr. Chauhan received POSOCO Power system Award (PPSA-2017) and Best PhD Thesis Award to recognize and reward innovative technical research excellence in power system. He has also received 2nd prize (Category Energy) in IEEE IAS CMD Humanitarian project contest 2017 for his project DC Micro-grid for Residential Buildings. He also received Bhaskara Advanced Solar Energy (BASE-2014) Award, for his research proposal in the area of DC microgrid from Department of Science and Technology, Indo-US Science and Technology Forum.
Content
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.
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.