ItemArticle

Deep learning-driven correction of motion-induced artifacts in microfluidic on-chip fluorescence microscopy for robust cell classification

Title
Deep learning-driven correction of motion-induced artifacts in microfluidic on-chip fluorescence microscopy for robust cell classification
Creator
Santhi, K.; Shrichandran, G.V.; Indhumathi, K.; Warrier, Gayathry S.; Sudha, S.; Rajaram, A.
Description
Fluorescence microscopy combined with microfluidic platforms allows for the analysis of single cells and the whole biomedical process to be done at high speed, however, it is often a very delicate method that can be heavily affected by motion-induced distortions during the high-speed flow. These artifacts, such as motion blur, misalignment, and shape deformation significantly lower automatical accuracy of the cell classification. The suggested research suggests that on-chip fluorescence microscopy employs an AI-based framework of distortion correction using Vision Transformers (ViT) and Generative Adversarial Networks (GAN) to remove motion artifacts in real-time. The combination of the GAN-ViT architecture does not only manage to reconstruct image quality but also to preserve fine cellular features when flowing system rates increase to 200 4L/min, which provide PSNR = 38.6 dB and SSIM = 0.98. When the system was used in both synthetic and experimental microfluidic data, it was able to reach a classification accuracy of 99.9, thereby indicating consistency in the system despite varying flow rates. The speed of the framework is 950 frames per second (fps), almost equal to the 1000-fps smartphone camera acquisition rate, thereby, demonstrating its suitability to the real-time, high-throughput imaging. As opposed to the past CNN or transformer techniques, a hybrid GAN-ViT architecture offered by the authors of this study directly implements in the imaging pipeline, thus enabling the simultaneous motion correction and diagnostic classification to occur immediately. The study results highlight the fact that AI-based distortion correction not only increases the accuracy of the diagnosis, but also personnel and laboratory response in microfluidic fluorescence microscopy. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026.
Source
Microfluidics and Nanofluidics;Volume;30;Issue;3;Article No.;25;
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Cancer cell screening; Deep learning; GAN; Microfluidic fluorescence microscopy; Motion artifact correction; Vision transformer
Coverage
Santhi K., Information Technology, Easwari Engineering College, Tamil Nadu, Chennai, 600089, India; Shrichandran G.V., Department of Computer Science and Engineering, School of Computer Science Engineering, SRM Institute of Science and Technology, Tamil Nadu, Ramapuram, Chennai, 600089, India; Indhumathi K., Assistant Professor, Department of Computer Applications, Kalasalingam Academy of Research and Education, Tamil Nadu, Krishnankoil, Srivilliputhur, 626126, India; Warrier G.S., Department of Computer Science, Christ (Deemed to be University, Karnataka, Bangalore, 560073, India; Sudha S., Professor,Department of Electronics and Communication Engineering, sri Ranganathar Institute of Engineering and Technology, Tamil Nadu, Coimbatore, 641110, India; Rajaram A., Department of Electronics and Communication Engineering, E.G.S Pillay Engineering College, Tamilnadu, Nagapattinam, 611002, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 16134982;
Format
online
Language
English
Type
Article

Citation

Santhi, K.; Shrichandran, G.V.; Indhumathi, K.; Warrier, Gayathry S.; Sudha, S.; Rajaram, A., “Deep learning-driven correction of motion-induced artifacts in microfluidic on-chip fluorescence microscopy for robust cell classification,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/21865.

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