Abstract

Protein, which serves as a service provider for the cell to exert specific function, is ubiquitously distributed in a biological system. After the transcription of genomic information, protein starts to form as the product of RNA translation. Therefore, in order to facilitate medical research in molecular biology, we need to develop complete and accurate models to identify and classify organelles in cells from a large number of images produced by microscopy. But the existing machine learning architectures are failed to provide the highest classification accuracy. Thus, this work majorly focused on implementation of Human Protein Atlas Image Classification using Deep learning Convolutional Neural Networks (DLCNN). The simulation results show that the propose method gives the highest classification accuracy compared to the state of art approaches.

Description