Mitosis detection from histological images using handcrafted features and artificial neural network

Hanan Hussain, Omar Hujran, K. P. Nitha

Research output: Contribution to journalArticlepeer-review

Abstract

Mitosis is defined as the rapid division of cells and its count is relevant to predict the grading of breast cancer. Since manual mitosis detection is time consuming and prone to errors, a fast and accurate detection approach is proposed using handcrafted features with artificial neural network (ANN). This method includes three steps: 1) image pre-processing involves conversion on RGB image to red-channel; 2) segmentation which is done using fuzzy C-means clustering and handcrafted features are extracted; 3) classification in which both random forest classifier and ANN are ensemble to predict the outcome. The system was tested with Mitos-Atypia14 dataset and an accuracy of 91.6% is obtained.

Original languageEnglish
Pages (from-to)240-256
Number of pages17
JournalInternational Journal of Computer Aided Engineering and Technology
Volume16
Issue number2
DOIs
Publication statusPublished - 2022

Keywords

  • ANN
  • Artificial neural network
  • Breast cancer detection
  • Mitosis detection
  • Random forest classifier

ASJC Scopus subject areas

  • Software
  • General Engineering
  • Computer Science Applications

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