All-optical classification of real biomedical cell images using a diffractive neural network: a simulation study
Abstract
We report an in-silico demonstration of an all-optical cell classification system using a single-layer diffractive neural network (DNN) optimized for real-world biomedical images. Implemented virtually with a spatial light modulator (SLM), the DNN was numerically trained via backpropagation to differentiate breast and lung cancer cells. The training utilized experimentally acquired phase and amplitude images from optofluidic time-stretch quantitative phase imaging. Classification was simulated by computing the optical intensities at the detection plane. The optimized DNN achieved 93.6% accuracy, approaching that of conventional convolutional neural networks. This study highlights the potential of SLM-based DNNs for ultrafast, energy-efficient biomedical image processing in practical optical computing scenarios.
Cite
@article{arxiv.2509.00370,
title = {All-optical classification of real biomedical cell images using a diffractive neural network: a simulation study},
author = {Norihide Sagami and Yueyun Weng and Cheng Lei and Ryosuke Oketani and Kotaro Hiramatsu},
journal= {arXiv preprint arXiv:2509.00370},
year = {2025}
}
Comments
4 pages, 4 figures