  Understanding and extracting the patterns of microscopy images has been a
major challenge in the biomedical field. Although trained scientists can locate
the proteins of interest within a human cell, this procedure is not efficient
and accurate enough to process a large amount of data and it often leads to
bias. To resolve this problem, we attempted to create an automatic image
classifier using Machine Learning to locate human proteins with higher speed
and accuracy than human beings. We implemented a Convolution Neural Network
with Residue and Squeeze-Excitation layers classifier to locate given proteins
of any type in a subcellular structure. After training the model using a series
of techniques, it can locate thousands of proteins in 27 different human cell
types into 28 subcellular locations, way significant than historical
approaches. The model can classify 4,500 images per minute with an accuracy of
63.07%, surpassing human performance in accuracy (by 35%) and speed. Because
our system can be implemented on different cell types, it opens a new vision of
understanding in the biomedical field. From the locational information of the
human proteins, doctors can easily detect cell's abnormal behaviors including
viral infection, pathogen invasion, and malignant tumor development. Given the
amount of data generalized by experiments are greater than that human can
analyze, the model cut down the human resources and time needed to analyze
data. Moreover, this locational information can be used in different scenarios
like subcellular engineering, medical care, and etiology inspection.
