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@article{203849,
        author = {Umamaheswararao Mogili},
        title = {An Advanced Convolutional Neural Network Based Framework for Intelligent Image Classification Using Tensorflow Deep Learning Architecture},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {1216-1225},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203849},
        abstract = {Image classification is one of the most important tasks in the field of computer vision, where images are automatically categorized into predefined classes based on their visual characteristics. With the exponential growth of digital image data, automated image classification systems have become increasingly important in various real-world applications, including medical imaging, security surveillance, autonomous vehicles, industrial automation, and object recognition systems. This paper presents an efficient image classification system developed using TensorFlow, a widely used deep learning framework introduced by Google. The proposed system utilizes Convolutional Neural Networks (CNNs) to automatically learn and extract significant features from input images and accurately classify them into multiple categories. The overall framework consists of image preprocessing, feature extraction, model training, and classification stages. To evaluate the effectiveness of the proposed model, the dataset is divided into training and testing sets. Experimental results show that the TensorFlow-based CNN model achieves high classification accuracy with efficient feature learning and improved generalization capability. Compared with traditional machine learning methods, the proposed deep learning approach provides superior classification performance, higher accuracy, and better automation in image recognition tasks.},
        keywords = {TensorFlow, Deep Learning, CNN, Computer Vision, ReLu.},
        month = {June},
        }