Intelligent CAD System for Automatic Detection of Mitotic Cells from Breast Cancer Histology Slide Images Based on Teaching-Learning-Based Optimization

Abstract

This paper introduces a computer-assisted diagnosis (CAD) system for automatic mitosis detection from breast cancer histopathology slide images. In this system, a new approach for reducing the number of false positives is proposed based on Teaching-Learning-Based optimization (TLBO). The proposed CAD system is implemented on the histopathology slide images acquired by Aperio XT scanner (scanner A). In TLBO algorithm, the number of false positives (falsely detected nonmitosis candidates as mitosis ones) is defined as a cost function and, by minimizing it, many of nonmitosis candidates will be removed. Then some color and texture (textural) features such as those derived from cooccurrence and run-length matrices are extracted from the remaining candidates and finally mitotic cells are classified using a specific support vector machine (SVM) classifier. The simulation results have proven the claims about the high performance and efficiency of the proposed CAD system.

Performance Evaluation of Cancer Diagnostics Using Autoregressive Features with SVM Classifier: Applications to Brain Cancer Histopathology

“…The average accuracy obtained through the k repetition of SVM classifier is considered as the final cancer detection accuracy. [39] …”

Automated mitosis detection in histopathology based on non-gaussian modeling of complex wavelet coefficients

“…Tashk et al [14] introduced an automatic mitosis detection method using completed local binary patterns based on a pixel-wise classification. In their recent publication [15], a CAD system was presented based on a teaching-learning-basedoptimization method to reduce false positives in mitosis detection. Tek [16] employed color, binary shape-based, Laplacian, and morphological features to perform mitosis classification.…”