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@article{195535,
        author = {Umamaheswararao Mogili},
        title = {A Convolutional Neural Network- Based Image Classification System Implemented Using Tensor flow},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {410-415},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195535},
        abstract = {Image classification is a fundamental problem in computer vision that aims to categorize images into predefined classes based on their visual features. With the rapid growth of digital image data, automatic image classification systems have become essential in many real-world applications such as medical diagnosis, security surveillance, autonomous vehicles, and object recognition systems. This paper proposes an image classification system using Tensor Flow, a popular deep learning framework developed by Google. The system uses Convolutional Neural Networks (CNN) to automatically extract meaningful features from images and classify them into different categories. The proposed model processes images through preprocessing, feature extraction, and classification stages. The dataset is divided into training and testing sets to evaluate the model performance. Experimental results demonstrate that the Tensor Flow-based deep learning model achieves high classification accuracy with efficient feature learning. The proposed system significantly improves classification performance compared to traditional machine learning approaches.},
        keywords = {Image Classification, Tensor Flow, Deep Learning, CNN, Computer Vision.},
        month = {April},
        }