A Multi-Feature Fusion Network for Pathological Identification of Tumor Cells

Abstract

A novel multi-feature fusion neural network (MFNet) is proposed to address the lack of applicability of existing medical aid diagnostic methods for cross-site and cross-tissue cytopathic lesion screening. MFNet consists of a data-sharing layer, a detailed feature transfer branch, a microscopic identification branch, and an auxiliary loss function. The data-sharing layer converts data images into a feature matrix and extracts detailed elements such as cell morphology, contour, and texture. The microscopic recognition branch obtains multilevel elements by convolving the input elements in stages and fusing them, so that the network can focus on high-level semantic elements such as minor differences of cytopathy. The detail feature transfer branch transfers detail elements across layers and achieves cross-layer fusion with semantic elements. The auxiliary loss function enables the network feature classification capability to be enhanced. MFNet is experimentally compared with AlexNet, VGG-16, ResNet-50, and other models on the tumor cell datasets, and the results show that the proposed method can effectively improve the recognition accuracy.

Keywords

• Tumour cells
• pathology recognition
• convolutional neural network
• multi-feature fusion