Going deeper: magnification‐invariant approach for breast cancer classification using histopathological images

Abstract

Abstract Breast cancer has the highest fatality for women compared with other types of cancer. Generally, early diagnosis of cancer is crucial to increase the chances of successful treatment. Early diagnosis is possible through physical examination, screening, and obtaining a biopsy of the dubious area. In essence, utilizing histopathology slides of biopsies is more efficient than using typical screening methods. Nevertheless, the diagnosing process is still tiresome and is prone to human error during slide preparation, such as when dyeing and imaging. Therefore, a novel method is proposed for diagnosing breast cancer into benign or malignant in a magnification‐specific binary (MSB) classification. Besides, the introduced method classifies each type into four subclasses in a magnification‐specific multi‐category (MSM) fashion. The proposed method involves normalizing the hematoxylin and eosin stains to enhance colour separation and contrast. Then, two types of novel features—deep and shallow features—are extracted using two deep structure networks based on DenseNet and Xception. Finally, a multi‐classifier method based on the maximum value is utilized to achieve the best performance. The proposed method is evaluated using the BreakHis histopathology data set, and the results in terms of diagnostic accuracy are promising, achieving 99% and 92% in terms of MSB and MSM, respectively, compared with recent state‐of‐the‐art methods reported in the survey conducted by Benhammou on the BreakHis data set using deep learning and texture‐based models.