Deep Learning Applied to Medical Image Aided Diagnosis Systems

DOI: https://doi.org/10.62517/jbdc.202401105

Author(s)

A Li mujiang·Tuersun1, Weiyang Fang2, Chaomin Chen2, Wei Wang3,*

Affiliation(s)

1Hospital of Integrated Traditional Chinese and Western Medicine, Southern Medical University, Guangzhou, China 2School of Biomedical Engineering, Southern Medical University, Guangzhou, China 3Tumor Hospital affiliated to Guangzhou Medical University, Guangzhou, China *Corresponding Author.

Abstract

At present, in the field of diagnostic medical imaging, only relying on doctors for diagnosis can no longer meet the needs of contemporary clinical development. Traditional computer-aided diagnostic systems are limited by their recognition ability and universality, and can only provide diagnostic decision-making references for doctors. With the in-depth application of artificial intelligence in the field of medical imaging, the application of deep learning technology in medical imaging-aided diagnosis systems, based on deep neural networks, can not only greatly reduce the workload of doctors, but also help to improve the disease screening ability and clinical diagnosis efficiency further. This paper carries out the research on the application of deep learning in the image-aided diagnosis system, and analyzes the application of deep learning in the image-aided diagnosis system. The study shows that deep learning has good research and application results in medical imaging, reflecting the advantages of deep learning to help improve the efficiency and accuracy of clinical diagnosis.

Keywords

Deep Learning; Convolutional Neural Network; Transfer Learning; Computer-Aided Diagnosis; Medical Image

References

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