mlsanityData to 7 Oct 2026

Research on Intelligent Aided Diagnosis System of Medical Image Based on Computer Deep Learning

arXiv 2404.18419 ↗First posted 29 Apr 2024cs.CV cs.AIcited by 8

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Abstract (arXiv)

This paper combines Struts and Hibernate two architectures together, using DAO (Data Access Object) to store and access data. Then a set of dual-mode humidity medical image library suitable for deep network is established, and a dual-mode medical image assisted diagnosis method based on the image is proposed. Through the test of various feature extraction methods, the optimal operating characteristic under curve product (AUROC) is 0.9985, the recall rate is 0.9814, and the accuracy is 0.9833. This method can be applied to clinical diagnosis, and it is a practical method. Any outpatient doctor can register quickly through the system, or log in to the platform to upload the image to obtain more accurate images. Through the system, each outpatient physician can quickly register or log in to the platform for image uploading, thus obtaining more accurate images. The segmentation of images can guide doctors in clinical departments. Then the image is analyzed to determine the location and nature of the tumor, so as to make targeted treatment.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 3 of its 21 references that are in the map. First posted on arXiv 29 Apr 2024.

Builds on

3 of 21 references.

References · 3

  1. 2023NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Spacecited by 88
  2. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  3. 2023Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNNcited by 35

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Citing papers per year: 2024: 8.

8 papers.

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