Deep Learning Based Image Classification Algorithm

We propose a new model which called MFAAM, specifically designed for image classification. By incorporating multi-scale feature fusion and adaptive attention mechanisms, it enhances both the accuracy and robustness of the classification process. The model's multi-scale feature fusion module extracts feature across various scales, effectively capturing both local details and global patterns within images. Meanwhile, the adaptive attention mechanism dynamically adjusts the significance of different image regions, enabling the model to prioritize key features during analysis. Moreover, this paper also proposes an efficient training strategy that combines data augmentation techniques and mixed precision training methods, further enhancing the generalization ability and training efficiency of the model. Experimental results show that the proposed model achieves outstanding classification performance on multiple standard datasets, demonstrating significant advantages in accuracy over existing image classification algorithms. These innovative designs provide new solutions and technical insights for future image classification tasks.