The 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 developed, 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 handcrafted 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 suffers from poor image (cell diffraction pattern) signatures due to their 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.

No abstract

No abstract

Digital Inline Holography (DIH) based microscopy is a well-established technique for the characterization of nano and microparticles, such as biological cells, artificial microparticles, quantum dots, etc. Due to its simplicity and cost-effectiveness, various practical solutions such as auto characterization of complete blood count (CBC), cell viability test, and 3D cell tomography have been developed. In our previous work, we demonstrated the feasibility of this system to perform complete blood count along with the auto characterization of cell-lines as well as shape and size characterization of the microparticles. However, its performance suffered due to the weak signals from some of the cells owing to their poor signatures and the presence of background noise. The auto characterization technique therein was based on the parameters determined from our empirical findings, which limit the system in terms of its cellline recognition power. In this work, we try to address these issues by leveraging an artificial intelligence-powered auto signal enhancing scheme as well as adaptive cell characterization technique. The performance comparison of our proposed method with the existing analytical model shows an increase in accuracy to >98% along with the signal enhancement of >5 dB for most cell types like Red Blood Cell (RBC) and White Blood Cell (WBC), except the cancer cells (HepG2 and MCF-7) for which the accuracy is about 84%.