Classification of the Human Protein Atlas Single Cell Using Deep Learning

Abstract

Deep learning has made great progress in many fields. One of the most important fields is the medical field, where we can classify images, detect objects and so on. More specifically, deep learning algorithms entered the field of single-cell classification and revolutionized this field, by classifying the components of the cell and identifying the location of the proteins in it. Due to the presence of large numbers of cells in the human body of different types and sizes, it was difficult to carry out analysis of cells and detection of components using traditional methods, which indicated a research gap that was filled with the introduction of deep learning in this field. We used the Human Atlas dataset which contains 87,224 images of single cells. We applied three novel deep learning algorithms, which are CSPNet, BoTNet, and ResNet. The results of the algorithms were promising in terms of accuracy: 95%, 93%, and 91%, respectively.

Keywords

• deep learning
• health and safety
• human protein atlas
• single cell classification