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Breast cancer is a great threat to the women population throughout the world. Due to the technological advancements in medical science and digital imaging technology, histopathological images are widely utilized for better diagnosis. However, the histopathological images involve complicated structure due to the inconsistent staining, lighting conditions and so on. Considering these challenges, this work presents a mitotic cell classification system based on supervised learning for histopathological images of breast cancer. As the classification solely depends on the effectiveness of nuclei extraction, the proposed approach employs twin stage segmentation for better nuclei extraction. The effectiveness of the proposed mitotic cell classification system is matched with the existing approaches and the proposed approach performs better than the existing works with respect to accuracy, sensitivity, specificity and F-measure rates.

Mitotic Cell Classification System Based On Supervised Learning for Histopathological Images of Breast Cancer