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Accurate localization of proteins from fluorescence microscopy images ischallenging due to the inter-class similarities and intra-class disparitiesintroducing grave concerns in addressing multi-class classification problems.Conventional machine learning-based image prediction pipelines rely heavily onpre-processing such as normalization and segmentation followed by hand-craftedfeature extraction to identify useful, informative, and application-specificfeatures. Here, we demonstrate that deep learning-based pipelines caneffectively classify protein images from different datasets. We propose anend-to-end Protein Localization Convolutional Neural Network (PLCNN) thatclassifies protein images more accurately and reliably. PLCNN processes rawimagery without involving any pre-processing steps and produces outputs withoutany customization or parameter adjustment for a particular dataset.Experimental analysis is performed on five benchmark datasets. PLCNNconsistently outperformed the existing state-of-the-art approaches fromtraditional machine learning and deep architectures. This study highlights theimportance of deep learning for the analysis of fluorescence microscopy proteinimagery. The proposed deep pipeline can better guide drug designing proceduresin the pharmaceutical industry and open new avenues for researchers incomputational biology and bioinformatics.

Deep localization of protein structures in fluorescence microscopy images