Open-source microscopic solution for classification of biological samples

Open-source technologies and solutions have paved the way for making science accessible the world over. Motivated to contribute to the direction of open-source methods, our current research presents a complete workflow of building a microscope using 3D printing and easily accessible optical components to collect images of biological samples. Further, these images are classified using machine learning algorithms to illustrate both the effectiveness of this method and show the disadvantages of classifying images that are visually similar. The second outcome of this research is an openly accessible dataset of the images collected, OPEN-BIOset, and made available to the machine learning community for future research. The research adopts the OpenFlexure Delta Stage microscope (https://openflexure.org/) that allows motorised control and maximum stability of the samples when imaging. A Raspberry Pi camera is used for imaging the samples in a transmission-based illumination setup. The imaging data collected is catalogued and organised for classification using TensorFlow. Using visual interpretation, we have created subsets from amongst the samples to experiment for the best classification results. We found that by removing similar samples, the categorical accuracy achieved was 99.9% and 99.59% for the training and testing sets. Our research shows evidence of the efficacy of open source tools and methods. Future approaches will use improved resolution images for classification and other modalities of microscopy will be realised based on the OpenFlexure microscope