Research on Image Classification Algorithms Based on Improved Convolutional Neural Networks

Traditional CNNs still face challenges when dealing with complex scenes and high-dimensional data, such as low feature extraction efficiency and model overfitting. To resolve these challenges, an enhanced convolutional neural network architecture, known as MCAPT, is proposed in this paper, with the goal of improving the accuracy and efficiency of image classification. Initially, a comprehensive analysis of the classic CNN model was conducted, revealing its deficiencies in the feature extraction and pooling layers. Consequently, a multi-scale convolution module was introduced, which utilizes convolution kernels of different sizes to extract image features in parallel. This module significantly enhances the model's capacity to represent a wide range of image characteristics. In addition, we employed adaptive pooling technology, which can flexibly adjust the size of the output feature maps, effectively reducing model complexity and computational burden. Finally, to prevent overfitting, this paper also introduces a new regularization method that combines different regularization techniques such as Dropout. This innovative measure significantly improves the model’s generalization ability, making its performance on the dataset more robust. Experimental results show that the improved CNN outperforms traditional CNN models on standard image classification benchmark datasets, verifying the effectiveness and superiority of the proposed algorithm. In summary, this paper not only provides an effective new algorithm for the field of image classification but also offers new insights for the further development of convolutional neural networks. Future research will focus on the real-time application and deployment of the algorithm to meet the urgent demands of the industry for high efficiency and accuracy.