FastLeakyResNet-CIR: A Novel Deep Learning Framework for Breast Cancer Detection and Classification

Abstract

Breast cancer is a type of disease that primarily affects the breast tissue, and it is crucial to achieve early diagnosis for successful treatment and recovery. In recent years, the residual network (ResNet) has gained significant attention in the detection of breast cancer using medical images. In this paper, we propose an efficient and robust deep learning framework called FastLeakyResNet-CIR, an improved ResNet architecture, for breast cancer detection and classification. The FastLeakyResNet-CIR achieves an impressive accuracy of 98.94% when evaluated on a dataset of 7909 microscopic images of breast tumor tissue from BreakHis dataset, which outperforms the state-of-the-art methods, e.g. ResNet18, ResNet50, InceptionV3 and VGG16. The experiment results further highlight the potential of FastLeakyResNet-CIR for accurate and rapid diagnosis of breast cancer, thus facilitating effective medical treatment for patients.

Keywords

• Breast cancer
• convolutional neural network
• FastLeakyResNet-CIR
• medical image classification
• residual network