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    Breast tumor detection in ultrasound images using artificial intelligence

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    Author
    Modi, Omkar
    Subasi, Abdulhamit cc
    Subject
    Artificial Intelligence (AI)
    Convolutional Neural Networks (CNNs)
    Transfer Learning (TL)
    Deep Learning (DL)
    Date
    2023-01-20
    
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    Abstract
    Leveraging artificial intelligence (AI) for categorizing breast tumors as malignant or benign from breast ultrasound images can provide an effective and relatively low-cost method for the diagnosis of breast cancer. Presently, many machine learning (ML) and deep learning (DL) algorithms have been used for early-stage breast cancer detection. AI algorithms have shown promising results in breast cancer detection tasks. The use of deep convolutional neural network approaches has provided solutions for the efficient analysis of breast ultrasound images. Convolutional neural network (CNN) models analyze the image data in multiple layers and extract features which helps in better feature extractions and better performance in comparison to the conventional ML algorithms. Apart from conventional learning algorithms, we use the transfer learning technique which uses knowledge from its previous training in another related problem set. In this chapter, we have demonstrated the use of DL models through transfer learning, deep feature extraction, machine learning models, and comparison of their performances.
    Department
    Computer Science
    Publisher
    Academic Press
    Book title
    Applications of Artificial Intelligence in Medical Imaging.
    DOI
    https://doi.org/10.1016/B978-0-443-18450-5.00003-7
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1016/B978-0-443-18450-5.00003-7
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