The development of digital medical imaging occupies a large proportion in modern medical care. With the continuous development of computer technology, intelligent medicine is inseparable from the identification of digital medical image information, which is playing an important role in clinical diagnosis and scientific research. In order to solve the shortcomings of existing digital medical image recognition research, based on the discussion of Gabor digital medical image functional equation and deep learning image semantic segmentation, this paper aims at the sample data and parameter settings of deep learning digital medical image recognition application. A brief introduction was given. And the design and discussion of the digital medical image recognition model structure of deep learning BP neural network, and finally the sensitivity (SE), specificity (SP), accuracy of the recognition results of the deep learning BP neural network digital medical image recognition model designed in this paper. The rate (AR) is experimentally compared with the (SVN) and (RNN) models. The experimental data show that the sensitivity (SE) and specificity (SP) of the deep learning BP neural network are compared with the (SVN) and (RNN) models. , the accuracy rate (AR) is higher, reaching an average of 0.91. It is significantly higher than the (SVN) and (RNN) models, so it is verified that the model designed in this paper has better classification effect, perception ability and discrimination ability in digital medical image recognition.

Huikai Wang, Yiming Jiang and Hua Zhuang. Deep Learning in Digital Medical Image Recognition. International Journal of Neural Network (2022), Vol. 3, Issue 2: 44-51. https://doi.org/10.38007/NN.2022.030206.

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