Nisreen I R Yassin1, Shaimaa Omran2, Enas M F El Houby3, Hemat Allam4. 1. Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt. Electronic address: eng_nesrin@hotmail.com. 2. Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt. Electronic address: shmomran@gmail.com. 3. Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt. Electronic address: em.fahmy@nrc.sci.eg. 4. Anaesthesia & Pain, Medical Division, National Research Centre, Dokki, Cairo 12311, Egypt. Electronic address: allamhemat@gmail.com.
Abstract
BACKGROUND AND OBJECTIVE: The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer. METHODS: The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included. However, the scope of this research is limited to scientific and academic works and excludes commercial interests. RESULTS: This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems.
BACKGROUND AND OBJECTIVE: The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer. METHODS: The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included. However, the scope of this research is limited to scientific and academic works and excludes commercial interests. RESULTS: This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems.
Authors: Laura Ferreira Pinheiro Nicolielo; Jeroen Van Dessel; G Harry van Lenthe; Ivo Lambrichts; Reinhilde Jacobs Journal: Br J Radiol Date: 2018-09-17 Impact factor: 3.039
Authors: Monica Marro; Anna M Rodríguez-Rivero; Cuauhtémoc Araujo-Andrade; Maria Teresa Fernández-Figueras; Laia Pérez-Roca; Eva Castellà; Jordi Navinés; Antonio Mariscal; Joan Francesc Julián; Pau Turon; Pablo Loza-Alvarez Journal: Cancers (Basel) Date: 2021-05-28 Impact factor: 6.639