Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.00370 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914014502584320 |
|---|---|
| author | Sagami, Norihide Weng, Yueyun Lei, Cheng Oketani, Ryosuke Hiramatsu, Kotaro |
| author_facet | Sagami, Norihide Weng, Yueyun Lei, Cheng Oketani, Ryosuke Hiramatsu, Kotaro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00370 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | All-optical classification of real biomedical cell images using a diffractive neural network: a simulation study Sagami, Norihide Weng, Yueyun Lei, Cheng Oketani, Ryosuke Hiramatsu, Kotaro Optics 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. |
| title | All-optical classification of real biomedical cell images using a diffractive neural network: a simulation study |
| topic | Optics |
| url | https://arxiv.org/abs/2509.00370 |