International Journal of Computer Techniques Volume 12 Issue 3 | Enhancing Image Classification using TensorFlow
Enhancing Image Classification using TensorFlow
International Journal of Computer Techniques – Volume 12 Issue 3, May – June 2025
Abstract
Image classification is a core component of **computer vision**, significantly advanced by **deep learning frameworks** like **TensorFlow**. This research explores **optimized CNN architectures, data augmentation, and transfer learning techniques** to enhance **classification accuracy while reducing training time**—critical for **autonomous systems and medical imaging applications**.
Keywords
Image Classification, TensorFlow, Transfer Learning, Data Augmentation, Optimization Techniques, Convolutional Neural Networks, Deep Learning.
Conclusion
The **TensorFlow-based image classification models** achieved up to **93.4% accuracy** using **transfer learning with MobileNetV2 and InceptionV3**, significantly improving **training speed and generalization**. Future directions may focus on **real-time deployment with TensorFlow Lite and further model optimization**.
References
- Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). “ImageNet Classification with Deep Convolutional Neural Networks.”
- Abadi, M. et al. (2016). “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.”
- Chollet, F. (2017). “Deep Learning with Python.” Manning Publications.
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