GDL

Journal from gdlhub / 2021-01-20 15:24:36
By : Muayad Sadik Croock, Saja Dhyaa Khuder, Ayad Esho Korial, Sahar Salman Mahmood, Telkomnika
Created : 2021-01-13, with 1 files

Keyword : breast cancer; deep-learning; software engineering; pattern recognition; website design;
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/14718
Document Source : web

The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.

Give Comment ?#(0) | Bookmark

Property	Value
Publisher ID	gdlhub
Organization	Telkomnika
Contact Name	Herti Yani, S.Kom
Address	Jln. Jenderal Sudirman
City	Jambi
Region	Jambi
Country	Indonesia
Phone	0741-35095
Fax	0741-35093
Administrator E-mail	elibrarystikom@gmail.com
CKO E-mail	elibrarystikom@gmail.com