A hybrid deep learning model for breast cancer diagnosis based on transfer learning and pulse-coupled neural networks.

Abstract

Radiology experts often face difficulties in mammography mass lesion labeling, which may lead to conclusive yet unnecessary and expensive breast biopsies. This paper focuses on building an automated diagnosis tool that supports radiologists in identifying and classifying mammography mass lesions. The paper's main contribution is to design a hybrid model based on Pulse-Coupled Neural Networks (PCNN) and Deep Convolutional Neural Networks (CNN). Due to the need for large datasets to train and tune CNNs, which are not available for medical images, Transfer Learning (TL) was exploited in this research. TL can be an effective approach when working with small-sized datasets. The paper's implementation was tested on three public benchmark datasets: DDMS, INbreast, and BCDR datasets for training and testing and MIAS for testing only. The results indicated the enhancement that PCNN provides when combined with CNN compared to other methods for the same public datasets. The hybrid model achieved 98.72% accuracy for DDMS, 97.5% for INbreast, and 96.94% for BCDR. To avoid overfitting, the proposed hybrid model was tested on an unseen MIAS dataset, achieving 98.77% accuracy. Other evaluation metrics are reported in the results section.