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 language | English |
|---|---|
| Pages (from-to) | 240-256 |
| Number of pages | 17 |
| Journal | International Journal of Computer Aided Engineering and Technology |
| Volume | 16 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2022 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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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