Navigating Image Classification: A Comprehensive Comparison of CNN and Transfer Learning Techniques

This research provides a thorough comparative evaluation of Convolutional Neural Networks (CNNs) and Transfer Learning (TL) approaches in performing Image Classification (IC) in different areas. While CNNs efficiently focus on local details in larger datasets, a number of their shortcomings are high training costs and inefficiency in handling little data. TL overcomes these drawbacks and does so more efficiently by employing already trained models, which considerably saves training times and improves the model’s performance, especially when data is limited. We explore and discuss the commonalities and differences of nine cutting-edge works that utilized CNN and TL approaches for IC, focusing on their techniques, datasets, and results. The results indicated that there was no situation where standard CNN models were more accurate or efficient when implemented against TL, including constraints of resources or availability of labeled data. The benefits of TL are demonstrated in this practical application of IC tasks, and as such the authors derive conclusions that will shape research and development in other sectors like health, agriculture, and industrial automation.