Introduction to artificial intelligence techniques for medical image analysis
Author
Subasi, Abdulhamit
Subject
Image AnalysisArtificial Neural Networks (ANN)
K-Nearest Neighbor (k-NN)
Decision Tree Algorithm
Support Vector Machine (SVM)
Random Forest
Bagging
Boosting
XGBoost
Deep Learning (DL)
LSTM
Convolutional Neural Networks (CNNs)
Clustering
Date
2023-01-20
Metadata
Show full item recordAbstract
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.Department
Computer SciencePublisher
Academic PressBook title
Applications of Artificial Intelligence in Medical Imaging.ae974a485f413a2113503eed53cd6c53
https://doi.org/10.1016/B978-0-443-18450-5.00010-4