A machine learning-based feature extraction method for image classification using ResNet architecture

With the development of Deep Learning, Convolutional Neural Networks (CNNs) have become a mainstream method for image classification, and the emergence of the ResNet architecture has significantly accelerated this process. However, as model depth increases, feature redundancy limits model performance. Although traditional machine learning methods like Principal Component Analysis (PCA) can effectively remove redundancy features, there is no effective method to integrate PCA as a feature extraction technique into different convolutional neural network architectures. This work proposes a Selective Principal Component Layer (SPCL), a feature extraction method that effectively incorporates PCA into convolutional neural networks to filter essential features and improve the feature representation ability of deep learning models. SPCL is applied to ResNet architecture models to reduce redundant features and enhance generalization performance in image classification tasks. Evaluations on CIFAR-10 and Tiny ImageNet datasets demonstrate its effectiveness. The results show SPCL can be generally applied to ResNet architecture models and improve model accuracy, balancing the improvement of model performance and stability without adding significant computational overhead, demonstrating its potential to enhance performance in complex image classification tasks.