Citation

Ting, F. F. and Sim, K. S. (2017) Self-regulated multilayer perceptron neural network for breast cancer classification. In: 2017 International Conference on Robotics, Automation and Sciences (ICORAS), 27-29 Nov. 2017, Melaka, Malaysia.

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Abstract

The algorithm named self-regulated multilayer perceptron neural network for breast cancer classification (MLNN) is designed for breast cancer classification. Conventionally, medical doctors need to manually delineate the suspicious breast cancer region. Many studies have suggested that segmentation manually is not only time consuming, but also machine and operator dependent. ML-NN utilise multilayer perceptron neural network on breast cancer classification to aid medical experts in diagnosis of breast cancer. Trained ML-NN can categorise the input medical images into benign, malignant and normal patients. By applying the present algorithm, breast medical images can be classified into cancer patient and normal patient without prior knowledge regarding the presence of cancer lesion. This method is aimed to assist medical experts for breast cancer patient diagnosis through implementation of supervised Multilayer Perceptron Neural Network. ML-NN can classified the input medical images as benign, malignant or normal patient with accuracy, specificity, sensitivity and AUC of 90.59%, 90.67% , 90.53% , and 0.906 ± 0.0227 respectively.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Supervised learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	24 Apr 2021 14:02
Last Modified:	24 Apr 2021 14:02
URII:	http://shdl.mmu.edu.my/id/eprint/7636

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