In oral health, deep learning is advancing diagnostics. Overall, good health must be maintained because several conditions of the mouth greatly destroy the quality of life in a person. Deep learning algorithms can predict how diseases may develop or progress through image analysis, clinical information, and patient histories. With such algorithms, one has the advantage of handling large amounts of data more accurately while still detecting things that dentists might not notice otherwise. However, this requires better datasets to be available. Additionally, it is important to ensure that healthcare professionals can understand and explain the models. Despite these setbacks though, deep learning offers hope for revolutionizing oral disease prediction and improving healthcare outcomes.


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    Titel :

    Advancing Oral Disease Diagnosis with Deep Learning and DenseNet Architecture


    Beteiligte:
    Vijayalakshmi, M. (Autor:in) / Tandon, Sajal (Autor:in) / Gupta, Tanay (Autor:in) / James, Sam T. (Autor:in)


    Erscheinungsdatum :

    06.11.2024


    Format / Umfang :

    813802 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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