Applications of Artificial Intelligence in Healthcare and Biomedicine Artificial Intelligence Applications in Healthcare and Medicine Series
Auteur : Subasi Abdulhamit
Applications of Artificial Intelligence in Healthcare and Biomedicine provides ?updated knowledge on the applications of artificial intelligence in medical image analysis. In 16 chapters, it presents artificial applications in Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyography (EMG), signal analysis, Computed Tomography (CT), Magnetic Resonance Imaging (MR), and Ultrasound image analysis. It equips researchers with tools for early breast cancer detection from mammograms using artificial intelligence (AI), AI models to detect lung cancer using histopathological image, and a deep learning-based approach to get a proper and faster diagnosis of the Optical Coherence Tomography (OCT) images. In addition, it presents 3D medical image analysis using 3D Convolutional Neural Networks (CNNs). Final sections cover an AI-based approach to forecast diabetes patients' hospital re-admissions. This is a valuable resource for clinicians, researchers, and healthcare professionals who are interested in learning more about the applications of Artificial Intelligence and its impact in medical/biomedical image analysis.
ABDULHAMIT SUBASI
2. Artificial intelligence-based emotion recognition using ECG signals
FADIME TOKMAK, ABDULHAMIT SUBASI, AND SAEED MIAN QAISAR
3. Artificial intelligence-based depression detection using EEG signals
FADIME TOKMAK AND ABDULHAMIT SUBASI
4. Electromyography signal classification using artificial intelligence
ABDULHAMIT SUBASI
5. An evaluation of pretrained convolutional neural networks for stroke classification from brain CT images
MUHAMMAD IRFAN, ABDULHAMIT SUBASI, NOMAN MUSTAFA, TOMI WESTERLUND, AND WEI CHEN
6. Brain tumor detection using deep learning from magnetic resonance images
EMAN HASSANAIN AND ABDULHAMIT SUBASI
7. Artificial intelligence-based fatty liver disease detection using ultrasound images
SAFDAR WAHID INAMDAR AND ABDULHAMIT SUBASI
8. Deep learning approaches for breast cancer detection using breast MRI
TANISHA SAHU AND ABDULHAMIT SUBASI
9. Automated detection of colon cancer from histopathological images using deep neural networks
MIRKA SUOMINEN, MUHAMMED ENES SUBASI, AND ABDULHAMIT SUBASI
10. Optical coherence tomography image classification for retinal disease detection using artificial intelligence
MUHAMMED ENES SUBASI, SOHAN PATNAIK, AND ABDULHAMIT SUBASI
11. Heart muscles inflammation (myocarditis) detection using artificial intelligence
RUPAL SHAH AND ABDULHAMIT SUBASI
12. Artificial intelligence for 3D medical image analysis
ABDULHAMIT SUBASI
13. Medical image segmentation using artificial intelligence
ABDULHAMIT SUBASI
14. DNA sequence classification using artificial intelligence
ABDULHAMIT SUBASI
15. Artificial intelligence in drug discovery and development
ABDULHAMIT SUBASI
16. Hospital readmission forecasting using artificial intelligence
ABDULHAMIT SUBASI
Abdulhamit Subasi is a highly specialized expert in the fields of Artificial Intelligence, Machine Learning, and Biomedical Signal and Image Processing. His extensive expertise in applying machine learning across diverse domains is evident in his numerous contributions, including the authorship of multiple book chapters, as well as the publication of a substantial body of research in esteemed journals and conferences. His career has spanned various prestigious institutions, including the Georgia Institute of Technology in Georgia, USA, where he served as a dedicated researcher. In recognition of his outstanding research contributions, Subasi received the prestigious Queen Effat Award for Excellence in Research in May 2018. His academic journey includes a tenure as a Professor of computer science at Effat University in Jeddah, Saudi Arabia, from 2015 to 2020. Since 2020, he has assumed the role of Professor of medical physics at the Faculty of Medicine, University of Turku in Turku, Finland
- Provides knowledge on Artificial Intelligence algorithms for clinical data analysis
- Gives insights into both AI applications in biomedical signal analysis, biomedical image analysis, and applications in healthcare, including drug discovery
- Equips researchers with tools for early breast cancer detection
Date de parution : 03-2024
Ouvrage de 548 p.
