BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images.

Rehman, Shams Ur; Khan, Muhamamd Attique; Masood, Anum; Almujally, Nouf Abdullah; Baili, Jamel; Alhaisoni, Majed; Tariq, Usman; Zhang, Yu-Dong

Rehman, Shams Ur; Khan, Muhamamd Attique; Masood, Anum; Almujally, Nouf Abdullah; Baili, Jamel; Alhaisoni, Majed; Tariq, Usman; Zhang, Yu-Dong.

Affiliation

Rehman SU; Department of Computer Science, HITEC University, Taxila 47080, Pakistan.
Khan MA; Department of Computer Science, HITEC University, Taxila 47080, Pakistan.
Masood A; Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway.
Almujally NA; Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Baili J; College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia.
Alhaisoni M; College of Computer Science and Engineering, University of Ha'il, Ha'il 81451, Saudi Arabia.
Tariq U; Management Information System Department, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia.
Zhang YD; Department of Informatics, University of Leicester, Leicester LE1 7RH, UK.

Diagnostics (Basel) ; 13(9)2023 May 03.

Article in En | MEDLINE | ID: mdl-37175009

Key words

breast cancer; data augmentation; deep learning; feature optimization; features fusion

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Full text: 1 Collection: 01-internacional Health context: 1_ASSA2030 / 2_ODS3 / 6_ODS3_enfermedades_notrasmisibles Database: MEDLINE Type of study: Screening_studies Language: En Journal: Diagnostics (Basel) Year: 2023 Document type: Article

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