Comparative study of Deep Learning and BERT-Based Models for Breast Cancer Detection

Abstract

Breast cancer is a prominent contributor to female deaths due to its prevalence, nearly twice as high as that of other cancers combined. Early detection of this disease is key to survival. This research examines the performance of the Deep Learning (DL) models such as RNN, LSTM and GRU along with BERT based models such as BERT-Base and BERT-Large in breast cancer detection application on structured medical datasets. The outcomes of the experiments showed that the model with 1-Layer RNN achieved the highest performance in terms of accuracy (97.36%), precision (97.56%), recall (95.24%), F₁ score (96.38%) and AUC (99.21%), while the model with 3-Layer RNN achieved the highest performance in terms of AUC (99.40%). The transformer-based models performed at 92.11% accuracy and 98.88% AUC for BERT-Base (Seed 21) while BERT-Large had inconsistent performance due to its higher complexity. The results show that the light-weight DL models are more reliable and efficient in a structured breast cancer data set.