A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms.

Samee, Nagwan Abdel; Alhussan, Amel A; Ghoneim, Vidan Fathi; Atteia, Ghada; Alkanhel, Reem; Al-Antari, Mugahed A; Kadah, Yasser M

Samee, Nagwan Abdel; Alhussan, Amel A; Ghoneim, Vidan Fathi; Atteia, Ghada; Alkanhel, Reem; Al-Antari, Mugahed A; Kadah, Yasser M.

Afiliación

Samee NA; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Alhussan AA; Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Ghoneim VF; Biomedical Engineering Department, Helwan University, Cairo 11795, Egypt.
Atteia G; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Alkanhel R; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Al-Antari MA; Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Korea.
Kadah YM; Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 22254, Saudi Arabia.

Sensors (Basel) ; 22(13)2022 Jun 30.

Article en En | MEDLINE | ID: mdl-35808433

Asunto(s)

Neoplasias de la Mama; Redes Neurales de la Computación; Neoplasias de la Mama/diagnóstico por imagen; Femenino; Humanos; Modelos Logísticos; Aprendizaje Automático; Mamografía/métodos

Palabras clave

CAD system; breast cancer; breast lesion classification; deep feature extraction and reduction; hybrid CNN-based LR-PCA

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Redes Neurales de la Computación Tipo de estudio: Diagnostic_studies / Risk_factors_studies Límite: Female / Humans Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Arabia Saudita

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