Introduction to artificial intelligence techniques for medical image analysis
dc.contributor.author | Subasi, Abdulhamit | |
dc.date.accessioned | 2023-03-13T10:02:13Z | |
dc.date.available | 2023-03-13T10:02:13Z | |
dc.date.issued | 2023-01-20 | |
dc.identifier.citation | Abdulhamit Subasi, Chapter 1 - Introduction to artificial intelligence techniques for medical image analysis, Editor(s): Abdulhamit Subasi, In Artificial Intelligence Applications in Healthcare&Medicine, Applications of Artificial Intelligence in Medical Imaging, Academic Press, 2023, Pages 1-49, ISBN 9780443184505, https://doi.org/10.1016/B978-0-443-18450-5.00010-4. (https://www.sciencedirect.com/science/article/pii/B9780443184505000104) | en_US |
dc.identifier.isbn | 9780443184505 | en_US |
dc.identifier.doi | https://doi.org/10.1016/B978-0-443-18450-5.00010-4 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.14131/628 | |
dc.description.abstract | As the main goal of artificial intelligence (AI) is to provide inference from a sample, it employs statistics theory to develop mathematical models. When a model is constructed, its description and algorithmic solution for understanding must be competent. In some cases, the AI algorithm’s competency may be just as crucial as its classification accuracy. AI is applied in a variety of domains, such as anomaly detection, forecasting, medical signal/image analysis as a decision support component, and so on. The goal of this chapter is to assist scientists in selecting an acceptable AI approach and then guiding them in determining the best strategy by utilizing medical imaging. Furthermore, to introduce readers with the fundamentals of AI before digging into tackling real-world issues with AI methodologies. Machine learning, deep learning, and transfer learning are examples of basic ideas discussed. Topics relating to the various AI methodologies, such as supervised and unsupervised learning, will be covered. As a result, the key AI algorithms are discussed briefly in this chapter. Relevant PYTHON programming codes and routines are provided in each section. | en_US |
dc.publisher | Academic Press | en_US |
dc.subject | Image Analysis | en_US |
dc.subject | Artificial Neural Networks (ANN) | en_US |
dc.subject | K-Nearest Neighbor (k-NN) | en_US |
dc.subject | Decision Tree Algorithm | en_US |
dc.subject | Support Vector Machine (SVM) | en_US |
dc.subject | Random Forest | en_US |
dc.subject | Bagging | en_US |
dc.subject | Boosting | en_US |
dc.subject | XGBoost | en_US |
dc.subject | Deep Learning (DL) | en_US |
dc.subject | LSTM | en_US |
dc.subject | Convolutional Neural Networks (CNNs) | en_US |
dc.subject | Clustering | en_US |
dc.title | Introduction to artificial intelligence techniques for medical image analysis | en_US |
dc.source.booktitle | Applications of Artificial Intelligence in Medical Imaging. | en_US |
dc.source.volume | Artificial Intelligence Applications in Healthcare&Medicine; | |
dc.source.pages | 1-49 | en_US |
dc.contributor.researcher | No Collaboration | en_US |
dc.contributor.lab | Artificial Intelligence & Cyber Security Lab | en_US |
dc.subject.KSA | HEALTH | en_US |
dc.source.index | Scopus | en_US |
dc.contributor.department | Computer Science | en_US |
dc.contributor.firstauthor | Subasi, Abdulhamit |