# Biosensors and Machine Learning for Enhanced Detection, Stratification, and Classification of Cells: A Review

arXiv: https://arxiv.org/abs/2101.01866 · HTML: https://mlsanity.com/paper/2101.01866/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 113 references that are in the map.
- First posted on arXiv 6 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [On the Computational Efficiency of Training Neural Networks](https://mlsanity.com/paper/1410.1141/) (2014)
- [DeepDownscale: a Deep Learning Strategy for High-Resolution Weather Forecast](https://mlsanity.com/paper/1808.05264/) (2018)
- [Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning](https://mlsanity.com/paper/2001.10695/) (2020)
- [Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry](https://mlsanity.com/paper/1904.09233/) (2019)
- [Nuclei Detection Using Mixture Density Networks](https://mlsanity.com/paper/1808.08279/) (2018)
- [Automated screening of sickle cells using a smartphone-based microscope and deep learning](https://mlsanity.com/paper/1912.05155/) (2019)
- [Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices](https://mlsanity.com/paper/1909.05393/) (2019)

## Abstract (arXiv)

Biological cells, by definition, are the basic units which contain the fundamental molecules of life of which all living things are composed. Understanding how they function and differentiating cells from one another therefore is of paramount importance for disease diagnostics as well as therapeutics. Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to Point-of-Care (POC) solutions with each passing day. Furthermore, Machine Learning has allowed for enhancement in analytical capabilities of these various biosensing modalities, especially the challenging task of classification of cells into various categories using a data-driven approach rather than physics-driven. In this review, we provide an account of how Machine Learning has been applied explicitly to sensors that detect and classify cells. We also provide a comparison of how different sensing modalities and algorithms affect the classifier accuracy and the dataset size required.