Traditional medical imaging detection methods still face issues such as insufficient precision in data processing efficiency, recognition accuracy, and early diagnosis capabilities. This paper constructs a medical information and health imaging detection system based on artificial intelligence technology. The system employs a stepwise modeling approach: firstly, convolutional neural networks (CNNs) are utilized for feature extraction and automatic segmentation of medical images; secondly, an attention mechanism is combined to enhance features in lesion areas; thirdly, a multi-modal deep fusion model integrates imaging data with patient structured information to improve the accuracy and reliability of diagnostic decisions; finally, an adaptive threshold algorithm optimizes detection results and enables visual presentation. Testing, using the LIDC-IDRI lung nodule image dataset as an example with a constructed training and validation set containing 5000 samples, shows that the system's average recognition accuracy stabilizes at 97.8%, and the average detection time is reduced to 2.3 seconds per case. This verifies the effectiveness and scalability of the method in automated and precise image detection.

Tao Zeng, Qianqian Xu, Rong Chen. Research on Medical Information Big Health Image Detection System under Artificial Intelligence Technology. International Journal of Multimedia Computing (2022), Vol. 3, Issue 3: 83-90. https://doi.org/10.38007/IJMC.2022.030306.

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