Recent trend analysis of convolutional neural network-based breast cancer diagnosis

Abstract

One of the most common malignancies worldwide is breast cancer. Early screening and diagnosis are important to the reduction of mortality rates of patients. In order to improve the performance and accuracy of breast cancer image screening, researchers have made significant progress in Computer-aided diagnosis (CAD) systems built on convolutional neural networks (CNN). In this research, several recent CNN models of breast cancer diagnosis are discussed and explained, and multiple public datasets of breast cancer images are introduced. The detailed performances of the models are presented and compared. The limitations and potential improvements of current CNN-based CAD are discussed. Convolution neural network-based CAD are still facing challenges of shortage of public dataset and the problem of implementation in the clinical scenario. Conclusively, using a convolutional neural network to diagnose breast cancer is still at its early stage, and further developments are required to apply convolutional neural network-based cancer diagnosis to clinical practices.

GSCEU-Net: An End-to-End Lightweight Skin Lesion Segmentation Model with Feature Fusion Based on U-Net Enhancements

“…U-Net is suitable for medical image segmentation because its architecture combines both low-level and high-level information. The low-level information helps improve accuracy, while the high-level information aids in extracting complex features, [26]. UNeXt [27], proposed by Valanarasu et al, combines MLP [22] with U-Net to achieve reduced parameter count while maintaining segmentation performance.…”