Transfer Learning-Based Framework for Low-Resource Image Classification

Abstract

Image classification in low-resource environments remains a significant challenge due to limited labeled data. This paper proposes a transfer learning-based framework that leverages pre-trained deep neural networks to improve classification accuracy with minimal training data. Fine-tuning strategies and data augmentation techniques are applied to enhance model generalization. Experimental results demonstrate that the proposed approach significantly outperforms models trained from scratch, making it effective for applications in medical imaging and remote sensing.