Transfer Learning-based CNN Model for the Classification of Breast Cancer from Histopathological Images

Abstract

Breast cancer can have significant emotional and physical repercussions for women and their families. The timely identification of potential breast cancer risks is crucial for prompt medical intervention and support. In this research, we introduce innovative methods for breast cancer detection, employing a Convolutional Neural Network (CNN) architecture and Transfer Learning (TL) technique. Our foundation is the ICAIR dataset, encompassing a diverse array of histopathological images. To harness the capabilities of deep learning and expand the model's knowledge base, we propose a TL model. The CNN component adeptly extracts spatial features from histopathological images, while the TL component incorporates pretrained weights into the model. To tackle challenges arising from limited labeled data and prevent overfitting, we employ ResNet152v2. Utilizing a pre-trained CNN model on extensive image datasets initializes our CNN component, enabling the network to learn pertinent features from histopathological images. The proposed model achieves commendable accuracy (96.47%), precision (96.24%), F1-score (97.18%), and recall (96.63%) in identifying potential breast cancer cases. This approach holds the potential to assist medical professionals in early breast cancer risk assessment and intervention, ultimately enhancing the quality of care for women's health.