Breast Cancer Prognosis Based on Transfer Learning Techniques in Deep Neural Networks

Abstract

Breast cancer is a major cause of death among women in both developed and underdeveloped countries. Early detection and diagnosis of breast cancer are crucial for patients to receive proper treatment and increase their chances of survival. To improve the automatic detection and diagnosis of breast cancer, a new deep learning model called “Breast Cancer Prognosis Based Transfer Learning (BCP-TL)” has been developed. This model uses transfer learning, which applies the knowledge gained from solving one problem to another relevant problem. The model is based on a pre-trained convolutional neural network (CNN) that extracts features from the mammographic image analysis society (MIAS) dataset. Four different CNN architectures were used in this
model: AlexNet, Xception, ResNeXt, and Channel Boosted CNN. The performance of the model was evaluated using six metrics, including accuracy, sensitivity, specificity, precision, F1-score, and the area under the ROC curve (AUC). The combination of Xception and Channel Boosted CNN showed excellent performance. By combining essential features from multiple iterations, the Channel Boosted CNN can achieve higher accuracy in breast cancer diagnosis, with an overall accuracy of 98.96%. This highlights the potential of the BCP-TL model in effectively detecting and diagnosing breast cancer.