Application of Neural Network Based on SIFT Local Feature Extraction in Medical Image Classification

作者：Cui, Shuqi^*; Jiang, Hong; Wang, Zheng; Shen, Chaomin

来源：2nd International Conference on Image, Vision and Computing (ICIVC), 2017-06-02 To 2017-06-04.

DOI：10.1109/ICIVC.2017.7984525

• SIFT
• slide the window
• SVM
• BP neural network
• ROI

In the medical image analysis, ROI (Region of Interest) is one of the key features of clinical diagnostic analysis. The applying of local features of ROI to the deep learning of image classification has the advantage of noise eliminating and information reducing. Based on existing research results, using Scale Invariant Feature Transformation (SIFT) algorithm combined with SVM classifier and sliding window to extract the local features and describe ROI precisely in the image. Finally, the extracted feature is used as the input layer of BP neural network in mammary gland X-ray image classification. The experimental results show that the accuracy of neural network classifier based on SIFT is 96.57%, which is 3.44% higher than that of traditional SVM classification accuracy. It is verified that our classifier is important to support clinical diagnosis and diagnosis.

• 出版日期2017
• 单位华东师范大学