Diatom classification via deep learning using raw holograms captured by a lenless holographic system

B. Bogue-Jimenez; R. Castañeda; C. Trujillo; A. Doblas

doi:10.1117/12.3001568

13 March 2024 Diatom classification via deep learning using raw holograms captured by a lenless holographic system

B. Bogue-Jimenez, R. Castañeda, C. Trujillo, A. Doblas

Proceedings Volume 12903, AI and Optical Data Sciences V; 129030H (2024) https://doi.org/10.1117/12.3001568
Event: SPIE OPTO, 2024, San Francisco, California, United States

Abstract

Digital Lensless Holographic Microscopy (DLHM) is a phase imaging modality that omits the use of lenses or other bulky hardware to recover information from microscopic objects. Deep learning models have been recently used to substitute traditional DLHM reconstruction algorithms and classify samples from the reconstructed amplitude and phase images. In this work, we have investigated using these models to classify diatom samples, circumventing the whole reconstruction process altogether. We have validated our approach using a simulated DLHM dataset by comparing the performance of three typical image-processing learning-based models: AlexNet, VGG16, and ResNet-18.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

B. Bogue-Jimenez, R. Castañeda, C. Trujillo, and A. Doblas "Diatom classification via deep learning using raw holograms captured by a lenless holographic system", Proc. SPIE 12903, AI and Optical Data Sciences V, 129030H (13 March 2024); https://doi.org/10.1117/12.3001568

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