Deep transfer with minority data augmentation for imbalanced breast cancer dataset

• Published in: Applied soft computing Vol. 97; p. 106759
• Main Authors: Saini, Manisha, Susan, Seba
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2020
• Subjects: Batch normalization; Data augmentation; DCGAN; Imbalanced dataset; Transfer learning; VGG16; Imbalanced dataset; DCGAN; Data augmentation; Transfer learning; VGG16; Batch normalization

Clinical diagnosis of breast cancer is a challenging problem in the biomedical domain. The BreakHis breast cancer histopathological image dataset consists of two classes: Benign (Minority class) and Malignant (Majority class). The imbalanced class distribution results in the degradation of performan...