BreaST-Net: Multi-Class Classification of Breast Cancer from Histopathological Images Using Ensemble of Swin Transformers

• Published in: Mathematics (Basel) Vol. 10; no. 21; p. 4109
• Main Authors: Tummala, Sudhakar, Kim, Jungeun, Kadry, Seifedine
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Automation; benign; Bias; Biopsy; Breast cancer; Classification; Computer vision; Datasets; Decision trees; Deep learning; Design and construction; Diagnosis; Diagnostic imaging; Electric transformers; Histology, Pathological; Histopathology; Image acquisition; Image classification; malignant; Mammography; Medical imaging; Methods; Neural networks; Nosology; Semantics; swin transformer; Transformers; Ultrasonic imaging; India
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math10214109

Breast cancer (BC) is one of the deadly forms of cancer, causing mortality worldwide in the female population. The standard imaging procedures for screening BC involve mammography and ultrasonography. However, these imaging procedures cannot differentiate subtypes of benign and malignant cancers. Here, histopathology images could provide better sensitivity toward benign and malignant cancer subtypes. Recently, vision transformers have been gaining attention in medical imaging due to their success in various computer vision tasks. Swin transformer (SwinT) is a variant of vision transformer that works on the concept of non-overlapping shifted windows and is a proven method for various vision detection tasks. Thus, in this study, we investigated the ability of an ensemble of SwinTs in the two-class classification of benign vs. malignant and eight-class classification of four benign and four malignant subtypes, using an openly available BreaKHis dataset containing 7909 histopathology images acquired at different zoom factors of 40×, 100×, 200×, and 400×. The ensemble of SwinTs (including tiny, small, base, and large) demonstrated an average test accuracy of 96.0% for the eight-class and 99.6% for the two-class classification, outperforming all the previous works. Thus, an ensemble of SwinTs could identify BC subtypes using histopathological images and may lead to pathologist relief.