Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{171893,
        author = {Swamini Hemant Barkale},
        title = {Classifying Proteins using ResNet50 and InceptionV3},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {1352-1356},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171893},
        abstract = {Proteins are termed as the executing blocks of the human cell. Proteins serve several functions like structural support provision, protection against diseases, detoxing and regulation of the passage of materials across cell membrane, to name a few. Historically, the classification of proteins in human cell has been limited to a singular pattern in one or a few type of cells. In order to fully understand the complexity and working of the human cell, it is necessary to develop models that would classify mixed patterns across a wide range of human cells. This work provides a comparative study between two convolutional neural network models like ResNet50 and InceptionV3 for multi label classification of various proteins present in human cell. Handling of class imbalance is also tackled in the same.},
        keywords = {Convolutional neural network, InceptionV3, Multi label classification, ResNet50},
        month = {January},
        }