Citation

Tan, Y. J. and Sim, K. S. and Ting, F. F. (2017) Breast cancer detection using convolutional neural networks for mammogram imaging system. In: 2017 International Conference on Robotics, Automation and Sciences (ICORAS), 27-29 Nov. 2017, Melaka, Malaysia.

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Abstract

In this paper, breast cancer detection using convolutional neural network for mammogram imaging system is proposed to classify mammogram image into normal, benign(noncancerous abnormality) and malignant (cancerous abnormality). Breast Cancer detection Using Convolutional Neural Networks (BCDCNN) is aimed to speed up the diagnosis process by assisting specialist to diagnosis and classification the breast cancer. A series of mammogram images are used to carry out preprocessing to convert a human visual image into a computer visual image and adjust suitable parameter for the CNN classifier. After that, all changed images are assigned into CNN classifier as training source. The CNN classifier will then produce a model to recognize the mammogram image. By comparing BCDCNN method with Mammogram Classification Using Convolutional Neural Networks (MCCNN), BCDCNN has improved the accuracy toward classification on the mammogram images.Thus, the results show that the proposed method has higher accuracy than other existing methods, mass only and all argument have been increased from 0.75 to 0.8585 and 0.608974 to 0.8271 accuracy

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Imaging system
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK8300-8360 Photoelectronic devices (General)
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	25 Apr 2021 14:25
Last Modified:	25 Apr 2021 14:25
URII:	http://shdl.mmu.edu.my/id/eprint/7644

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