Convolutional neural network improvement for breast cancer classification

Fung Fung Ting, Yen Jun Tan, Kok Swee Sim

Research output: Contribution to journalArticleResearchpeer-review

299 Citations (Scopus)


Traditionally, physicians need to manually delineate the suspected breast cancer area. Numerous studies have mentioned that manual segmentation takes time, and depends on the machine and the operator. The algorithm called Convolutional Neural Network Improvement for Breast Cancer Classification (CNNI-BCC) is presented to assist medical experts in breast cancer diagnosis in timely manner. The CNNI-BCC uses a convolutional neural network that improves the breast cancer lesion classification in order to help experts for breast cancer diagnosis. CNNI-BCC can classify incoming breast cancer medical images into malignant, benign, and healthy patients. The application of present algorithm can assist in classification of mammographic medical images into benign patient, malignant patient and healthy patient without prior information of the presence of a cancerous lesion. The presented method aims to help medical experts for the classification of breast cancer lesion through the implementation of convolutional neural network for the classification of breast cancer. CNNI-BCC can categorize incoming medical images as malignant, benign or normal patient with sensitivity, accuracy, area under the receiver operating characteristic curve (AUC) and specificity of 89.47%, 90.50%, 0.901 ± 0.0314 and 90.71% respectively.

Original languageEnglish
Pages (from-to)103-115
Number of pages13
JournalExpert Systems with Applications
Publication statusPublished - 15 Apr 2019
Externally publishedYes


  • Artificial neural network
  • Breast cancer classification
  • Image processing
  • Medical imaging
  • Supervised learning

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