Design of Network Medical Image Information Feature Diagnosis Method Based on Big Data

• Published in: Mobile networks and applications Vol. 28; no. 5; pp. 1751 - 1761
• Main Authors: Li, Wei, Liu, Hui
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2023; Springer Nature B.V
• Subjects: Algorithms; Belief networks; Big Data; Communications Engineering; Computer Communication Networks; Diagnosis; Diagnostic systems; Disease; Electrical Engineering; Engineering; Ensemble learning; Feature extraction; IT in Business; Machine learning; Medical diagnosis; Medical imaging; Networks; Search algorithms; Swarm intelligence; Network medical image; Information feature; Big data; Deep belief network; Disease diagnosis; t-SNE algorithm
• ISSN: 1383-469X; 1572-8153
• DOI: 10.1007/s11036-023-02237-0

In the context of "smart healthcare", due to the substantial increase in medical data and patient diagnostic needs, conventional diagnostic methods are gradually unable to meet the current diagnostic requirements. Therefore, a network medical image information feature diagnosis method based on big data is designed to improve the effect of disease diagnosis. The convolutional deep belief network is used to extract the information features of the network medical image in the network medical image. The t-SNE algorithm is used to select the more valuable network medical image information features in the extracted features. Using stacking to integrate AdaBoost and Bagging algorithm, the disease diagnosis results are obtained. The artificial bee colony algorithm is used to optimize the weights of the multi-level ensemble learning algorithm to improve the accuracy of disease diagnosis. In the multi-level ensemble learning algorithm after weight optimization, the selected network medical image information features are input and the disease diagnosis results are output. Experiments show that this method can effectively extract the information features of network medical images and accurately diagnose diseases. At different spatial resolutions of network medical images, the Kappa values of disease diagnosis of this method are high, and the lowest Kappa value is about 0.875, which means that this method has high disease diagnosis performance.