A Hybrid Approach with Xception and NasNet for Early Breast Cancer Detection

Abstract

Breast cancer is the most common cancer in women, accounting for 12.5% of global cancer cases in 2020, and the leading cause of cancer deaths in women worldwide. Early detection is therefore crucial to reducing deaths, and recent studies suggest that deep learning techniques can detect breast cancer more accurately than experienced doctors. Experienced doctors can detect breast cancer with only 79% accuracy, while machine learning techniques can achieve up to 91% accuracy (and sometimes up to 97%). To improve breast cancer classification, we conducted a study using two deep learning models, Xception and NasNet, which we combined to achieve better results in distinguishing between malignant and benign tumours in digital databases and cell images obtained from mammograms. Our hybrid model showed good classification results, with an accuracy of over 96.2% and an AUC of 0.993 (99.3%) for mammography data. Remarkably, these results outperformed all other models we compared them with, Top of Form ResNet101 and VGG, which only achieved accuracies of 87%, 88% and 84.4% respectively. Our results were also the best in the field, surpassing the accuracy of other recent hybrid models such as MOD-RES + NasMobile with 89.50% accuracy and VGG 16 + LR with 92.60% accuracy. By achieving this high accuracy rate, our work can make a significant contribution to reducing breast cancer deaths worldwide by helping doctors to detect the disease early and begin treatment immediately.