Multiple semantic X-ray medical image retrieval using efficient feature vector extracted by FPN

Article type: Research Article

Authors: Zhi, Lijia^{a; b} | Duan, Shaoyong^a | Zhang, Shaomin^{a; b; *}

Affiliations: [a] School of Computer Science and Engineering, North Minzu University, Yinchuan, China | [b] Medical Imaging Center, Ningxia Hui Autonomous Region People’s Hospital, Yinchuan, China

Correspondence: [*] Corresponding author: Shaomin Zhang. E-mail: shmzhang_paper@163.com.

Abstract: OBJECTIVE:Content-based medical image retrieval (CBMIR) has become an important part of computer-aided diagnostics (CAD) systems. The complex medical semantic information inherent in medical images is the most difficult part to improve the accuracy of image retrieval. Highly expressive feature vectors play a crucial role in the search process. In this paper, we propose an effective deep convolutional neural network (CNN) model to extract concise feature vectors for multiple semantic X-ray medical image retrieval. METHODS:We build a feature pyramid based CNN model with ResNet50V2 backbone to extract multi-level semantic information. And we use the well-known public multiple semantic annotated X-ray medical image data set IRMA to train and test the proposed model. RESULTS:Our method achieves an IRMA error of 32.2, which is the best score compared to the existing literature on this dataset. CONCLUSIONS:The proposed CNN model can effectively extract multi-level semantic information from X-ray medical images. The concise feature vectors can improve the retrieval accuracy of multi-semantic and unevenly distributed X-ray medical images.

Keywords: CBMIR, multiple semantic, retrieval, X-Ray image, IRMA

DOI: 10.3233/XST-240069

Journal: Journal of X-Ray Science and Technology, vol. 32, no. 5, pp. 1297-1313, 2024