A Hybridized Deep Learning Methodology for Mitosis Detection and Classification from Histopathology Images

Abstract

Pathologists use histopathological images to diagnose cancer, and one key step in this process is to detect and classify mitosis. Mitosis is the process of cell division, and it is essential for normal tissue growth and repair. However, abnormal mitosis can be a sign of cancer. Therefore, the ability to accurately detect and classify mitosis is crucial for cancer diagnosis. Traditionally, pathologists rely on manual methods for this task, which are labor-intensive, time-consuming, and expensive. Computer-aided diagnosis (CAD) leverages technologies like artificial intelligence, fuzzy logic, and image processing to assist pathologists in early detection and classification. This study introduces a hybridized methodology for detecting and classifying abnormal mitosis in breast histopathological images. The proposed approach comprises two stages. In the initial stage, deep learning techniques are employed for mitosis detection. Subsequently, fuzzy-based classifiers are utilized in the second stage for mitosis classification. The methodology is applied to the ICPR12 and ICPR14 mitosis datasets. Results indicate a substantial enhancement in both accuracy and reliability of mitosis detection and classification, showcasing the effectiveness of the proposed approach.