Deep Learning-based Mammogram Classification using Small Dataset

Breast Cancer is one of the most diagnosed cancer and the leading cause of death among women worldwide, second only to lung cancer. Mammographic screening has been the most successful screening technology capable of detecting up to 90% of all breast cancer even before a lump growth can be felt using breast exam. However, mammogram is a low intensity image and the heterogeneous nature of breast can make healthy breast tissue appears as cancerous, this is most common among women with dense breast (aged 40-44). Thus, the sensitivity for early detection of breast cancer from mammogram has been estimated to 85-90%. This result can be improved by Deep CNN, however, to achieve good generalization, it must be train with high voluminous dataset whereas, mammographic dataset exists in smaller volume. In this paper, we present a method of training deep CNN with few datasets to achieve high training result and good generalization. An augmentation technique that increase both size and variance of the dataset is presented herewith, the augmented dataset was used to train five state of the art models. Highest training and validation accuracy (99.01% and 99.99% respectively) were achieved with DensNet. Meanwhile, SqueezeNet, a deep CNN model with fewer parameter also shows promising result, which means soon this model can be deployed into microcontroller and FPGAs for clinical applications.