Study of Image Processing Techniques for Enhancing the Performance of Convolutional Neural Network

Convolutional Neural Networks (CNNs) have become the keystone of modern image processing due to their ability to automatically learn feature representations. This paper focus on image processing techniques that significantly improve CNN performance and its effectiveness. Data augmentation, particularly advanced methods like Mixup and Cutout, expands training datasets and helps prevent over fitting by introducing diverse, synthetic variations of the data. Neural architecture search (NAS) to optimize network structures for specific tasks, improving accuracy and reducing computational costs. Transfer learning, especially using larger pre-trained models, has proven beneficial for tasks with limited labeled data, accelerating training and improving generalization. Advanced regularization techniques, such as the use of Spatial Dropout and Batch Re-normalization, stabilize learning by addressing issues like internal covariate shift and over fitting. The Squeeze-and-Excitation (SE) block have shown improvements in feature selection and enhanced feature extraction.