A Large-Scale Fully Annotated Low-Cost Microscopy Image Dataset for Deep Learning Framework

This work presents a large-scale three-fold annotated, low-cost microscopy image dataset of potato tubers for plant cell analysis in deep learning (DL) framework which has huge potential in the advancement of plant cell biology research. Indeed, low-cost microscopes coupled with new generation smartphones could open new aspects in DL-based microscopy image analysis, which offers several benefits including portability, easy to use, and maintenance. However, its successful implications demand properly annotated large number of diverse microscopy images, which has not been addressed properly- that confines the advanced image processing based plant cell research. Therefore, in this work, a low-cost microscopy image database of potato tuber cells having total 34,657 number of images, has been generated by Foldscope (costs around 1 USD) coupled with a smartphone. This dataset includes 13,369 unstained and 21,288 stained (safranin-o, toluidine blue-o, and lugol's iodine) images with three-fold annotation based on weight, section areas, and tissue zones of the tubers. The physical image quality (e.g., contrast, focus, geometrical attributes, etc.) and its applicability in the DL framework (CNN-based multi-class and multi-label classification) have been examined and results are compared with the traditional microscope image set. The results show that the dataset is highly compatible for the DL framework.