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    Introduction to artificial intelligence techniques for medical image analysis

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    Author
    Subasi, Abdulhamit cc
    Subject
    Image Analysis
    Artificial 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
    Show allShow less
    Date
    2023-01-20
    
    Metadata
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    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.
    Department
    Computer Science
    Publisher
    Academic Press
    Book title
    Applications of Artificial Intelligence in Medical Imaging.
    DOI
    https://doi.org/10.1016/B978-0-443-18450-5.00010-4
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1016/B978-0-443-18450-5.00010-4
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