A Research Paper on Deep Learning Based Image Classification Using the Tensor Flow Framework

This study demonstrates the effective use of TensorFlow for building a CNN-based image classification model. The results confirm that convolutional neural networks, combined with regularization techniques such as dropout and data augmentation, can achieve strong classification performance even on relatively small and low-resolution datasets like CIFAR-10. TensorFlow's flexible, high-level API enables rapid prototyping and efficient training of deep learning models, making it a valuable tool for both academic research and real-world computer vision applications. Future work may explore transfer learning using pretrained architectures such as ResNet or MobileNet to further improve classification accuracy.

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