Deep learning Framework for Mobile Microscopy

TL;DR

This paper presents a comprehensive deep learning pipeline tailored for mobile microscopy, addressing key challenges like focus classification, deblurring, and image fusion to improve image quality for disease diagnostics.

Contribution

It introduces a set of specialized CNN and GAN-based models optimized for mobile microscopy, enhancing automation and image quality in high-throughput settings.

Findings

Effective in-focus/out-of-focus classification achieved

DeblurGAN-based deblurring improves image clarity

FuseGAN enhances detail by combining multiple images

Abstract

Mobile microscopy is a promising technology to assist and to accelerate disease diagnostics, with its widespread adoption being hindered by the mediocre quality of acquired images. Although some paired image translation and super-resolution approaches for mobile microscopy have emerged, a set of essential challenges, necessary for automating it in a high-throughput setting, still await to be addressed. The issues like in-focus/out-of-focus classification, fast scanning deblurring, focus-stacking, etc. -- all have specific peculiarities when the data are recorded using a mobile device. In this work, we aspire to create a comprehensive pipeline by connecting a set of methods purposely tuned to mobile microscopy: (1) a CNN model for stable in-focus / out-of-focus classification, (2) modified DeblurGAN architecture for image deblurring, (3) FuseGAN model for combining in-focus parts from multiple images to boost the detail. We discuss the limitations of the existing solutions developed for professional clinical microscopes, propose corresponding improvements, and compare to the other state-of-the-art mobile analytics solutions.