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Machine and Deep Learning for Automated Diagnosis of Critical diseases
Methods, Algorithms and Applications
- 1st Edition - December 1, 2099
- Editors: Kalpana Chauhan, Rajeev Kumar Chauhan
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 3 1 0 - 7
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 3 1 1 - 4
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… Read more
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Request a sales quoteWhen 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.
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.
- Presents the applications of artificial intelligence and machine learning techniques to medical imaging
- Includes advanced techniques those addresses automatic diagnosis of various diseases and techniques to improve the diagnosis methods
- Describes how deep learning methods and approaches have been applied for the diagnosis of disease
- Includes a chapter on interpretability of the models
- Presents explainability in the context of medical imaging
- Includes a chapter on future directions and challenges
Engineers and computer science researchers in medical imaging, graduate students; Clinical researchers and clinicians
Part 1: Role of Artificial intelligence and machine learning in medical diagnosis
1. Challenges for implementing the artificial intelligence and machine learning in the diagnosis of diseases
2. Effective techniques for particular diagnosis
Part 2: Machine Learning based diagnosis techniques
3. Artificial intelligent technique for automated diagnosis of coronary artery disease
4. Artificial intelligent technique for automated diagnosis of lung infection
5. Artificial intelligent technique for automated diagnosis of tumors
Part 3: Deep Learning based diagnosis techniques
6. Supervised machine learning methods for diagnosis and severity identification of diseases
7. Unsupervised learning methods for diagnosis and severity identification of diseases
Part 4: Neural Network and Fuzzy Algorithms
8. Neuro Network models for diagnosis of medical diseases
9. Fuzzy models for diagnosis of medical diseases
10. Neuro-Fuzzy hybrid models for diagnosis of medical diseases
Part 5: Case Studies on various modalities
11. Progressive analysis of cancer or lung disease
12. Sepsis and septic shock prediction using machine learning models
Part 6: Future advancement and challenges with machine learning
13. Integration of AI and IoT for the prediction and analysis of diseases records
14. Prediction of severity growth of cancerous diseases
15. Real-time AI models for medical image processing
16. Medical issues and adaptation of machine learning and deep learning in clinical diagnosis
17. Machine learning explainability in medical applications
18. Interpretability of machine learning‐based prediction models for various diseases
1. Challenges for implementing the artificial intelligence and machine learning in the diagnosis of diseases
2. Effective techniques for particular diagnosis
Part 2: Machine Learning based diagnosis techniques
3. Artificial intelligent technique for automated diagnosis of coronary artery disease
4. Artificial intelligent technique for automated diagnosis of lung infection
5. Artificial intelligent technique for automated diagnosis of tumors
Part 3: Deep Learning based diagnosis techniques
6. Supervised machine learning methods for diagnosis and severity identification of diseases
7. Unsupervised learning methods for diagnosis and severity identification of diseases
Part 4: Neural Network and Fuzzy Algorithms
8. Neuro Network models for diagnosis of medical diseases
9. Fuzzy models for diagnosis of medical diseases
10. Neuro-Fuzzy hybrid models for diagnosis of medical diseases
Part 5: Case Studies on various modalities
11. Progressive analysis of cancer or lung disease
12. Sepsis and septic shock prediction using machine learning models
Part 6: Future advancement and challenges with machine learning
13. Integration of AI and IoT for the prediction and analysis of diseases records
14. Prediction of severity growth of cancerous diseases
15. Real-time AI models for medical image processing
16. Medical issues and adaptation of machine learning and deep learning in clinical diagnosis
17. Machine learning explainability in medical applications
18. Interpretability of machine learning‐based prediction models for various diseases
- No. of pages: 475
- Language: English
- Edition: 1
- Published: December 1, 2099
- Imprint: Academic Press
- Paperback ISBN: 9780443133107
- eBook ISBN: 9780443133114
KC
Kalpana Chauhan
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.
Affiliations and expertise
Assistant Professor, Department of Electrical Engineering, Central University of Haryana, Mahendargarh, IndiaRC
Rajeev Kumar Chauhan
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.
Affiliations and expertise
Assistant Professor, Department of Electrical Engineering, Dayalbagh Educational Institute, Agra, India