Efficacy of MobileNet Models in Detecting Breast Cancer in Patient Histopathology Images – An Empirical Examination.

Breast cancer, the most common of all the cancers, is treatable when detected early. Histopathology (HP) biopsy images generated from breast tissue samples are commonly used as early screening tools for detecting malignancy. This study has developed and compared the efficacy of two convolutional neural network (CNN) models that are based on MobileNet architecture for automatic detection of Invasive Ductal Carcinoma (IDC), the most common form of breast cancer, in histopathology (HP) images of breast tissues. The best of the two models has shown encouraging classification performance in terms of accuracy, precision and recall. The results attest to the viability of using lighter CNN models such as MobileNet for decision support when screening for breast cancer. [ABSTRACT FROM AUTHOR]