Leukocyte detection based on convolutional neural network fusion with color Fourier ptychographic microscopy

Abstract

Abstract In the field of optical microscopic imaging, color Fourier Ptychographic Microscopy (FPM) technology has attracted much attention due to its advantages of large field of view, high resolution, and quantitative phase imaging. In this paper, a color FPM fusion algorithm based on deep learning is proposed in combination with Convolutional Neural Networks (CNN) and applied to leukocyte detection. Firstly, this paper introduces a fusion model of a convolutional neural network based on the traditional color FPM imaging method and fuses low-resolution color images and high-resolution grayscale images through a multilayer convolutional network. This method improves the quality of reconstructed images while reducing the reconstruction time. Secondly, this paper constructs a leukocyte detection dataset by using an improved color FPM reconstruction algorithm and builds a leukocyte detection system based on the YOLOv7 architecture. This paper shows that combining convolutional neural networks with color FPM technology can provide higher-quality reconstructed images in medical imaging and cell detection, which provides strong technical support for digital pathology and medical diagnosis.