Machine learning based lens-free imaging technique for field-portable cytometry

TL;DR

This paper introduces an AI-enhanced lens-free imaging method for portable cytometry, significantly improving accuracy and adaptability in cell classification by using deep learning for signal enhancement and transfer learning.

Contribution

It develops a deep neural network-based auto signal enhancement and adaptive classification system for lens-free cytometry, overcoming previous limitations of hand-crafted features and poor image quality.

Findings

Accuracy >98% in cell classification

Signal enhancement >5 dB for cell diffraction patterns

Model adapts quickly to new cell types

Abstract

Lens-free Shadow Imaging Technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to its simplicity and cost-effectiveness, various low-cost solutions have been evolved, such as automatic analysis of complete blood count (CBC), cell viability, 2D cell morphology, 3D cell tomography, etc. The developed auto characterization algorithm so far for this custom-developed LSIT cytometer was based on the hand-crafted features of the cell diffraction patterns from the LSIT cytometer, that were determined from our empirical findings on thousands of samples of individual cell types, which limit the system in terms of induction of a new cell type for auto classification or characterization. Further, its performance is suffering from poor image (cell diffraction pattern) signatures due to its small signal or background noise. In this work, we address these issues by leveraging the artificial intelligence-powered auto signal enhancing scheme such as denoising autoencoder and adaptive cell characterization technique based on the transfer of learning in deep neural networks. The performance of our proposed method shows an increase in accuracy >98% along with the signal enhancement of >5 dB for most of the cell types, such as Red Blood Cell (RBC) and White Blood Cell (WBC). Furthermore, the model is adaptive to learn new type of samples within a few learning iterations and able to successfully classify the newly introduced sample along with the existing other sample types.