Image processing based on neural networks
Boming Yang · Applied and Computational Engineering · 2023
This article integrates convolutional neural network (CNN) and graph convolutional network (GCN) techniques. Performance is improved by the suggested architecture's use of crucial methods such as dropout, batch normalization, and rank-based random pooling. The network was trained and tested on a sizable amount of breast lesion imaging data, and its precision was evaluated in comparison to other methods. The findings showed a significant increase in accuracy, resulting in high rates of malignant lesion diagnosis with few false positives. The effective fusion of CNN and GCN methods highlights the potential for improving the detection of malignant breast lesions and provides a viable path for further study in this area.