Self-regulated multilayer perceptron neural network for breast cancer classification

F. F. Ting, K. S. Sim

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19 Citations (Scopus)


The algorithm named self-regulated multilayer perceptron neural network for breast cancer classification (ML-NN) 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.

Original languageEnglish
Title of host publicationProceedings of 2017 International Conference on Robotics, Automation and Sciences ICORAS 2017
EditorsSim Kok Swee
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781538619087
ISBN (Print)9781538619094
Publication statusPublished - 2017
Externally publishedYes
EventInternational Conference on Robotics, Automation and Sciences 2017 - Melaka, Malaysia
Duration: 27 Nov 201729 Nov 2017 (Proceedings)


ConferenceInternational Conference on Robotics, Automation and Sciences 2017
Abbreviated titleICORAS 2017
Internet address


  • breast cancer classification
  • image processing
  • medical imaging
  • multilayer neural network
  • supervised learning

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