Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network

• Published in: BioMed research international Vol. 2020; no. 2020; pp. 1 - 12
• Main Authors: Zhang, Shaomin, Zhou, Tao, Zhi, Lijia
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc
• Subjects: Artificial neural networks; Classification; Datasets; Decomposition; Deep learning; Diagnostic imaging; Digital imaging; Empirical analysis; Error analysis; Health aspects; Image analysis; Image management; Image processing; Image retrieval; Magnetic resonance imaging; Medical imaging; Medical technology; Methods; Neural networks; Search process; Semantics; Subject specialists; China
• ISSN: 2314-6133; 2314-6141; 2314-6141
• DOI: 10.1155/2020/6687733

Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The efficacy of high-level medical information representation using features is a major challenge in CBMIR systems. Features play a vital role in the accuracy and speed of the search process. In this paper, we propose a deep convolutional neural network- (CNN-) based framework to learn concise feature vector for medical image retrieval. The medical images are decomposed into five components using empirical mode decomposition (EMD). The deep CNN is trained in a supervised way with multicomponent input, and the learned features are used to retrieve medical images. The IRMA dataset, containing 11,000 X-ray images, 116 classes, is used to validate the proposed method. We achieve a total IRMA error of 43.21 and a mean average precision of 0.86 for retrieval task and IRMA error of 68.48 and F1 measure of 0.66 on classification task, which is the best result compared with existing literature for this dataset.