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Deep Learning-based Image Recognition Technology in Medical Diagnosis

Xia Wu

Xia Wu

Department of Information Engineering, Henan Vocational College of Water Conservancy and EnvironmentZhengzhou, China
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Wu, Xia
  
Feb 03, 2025
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Published Online: Feb 03, 2025
Received: Sep 16, 2024
Accepted: Jan 04, 2025
DOI: https://doi.org/10.2478/amns-2025-0008
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© 2025 Xia Wu, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Residual learning
Residual learning

Figure 2.

Mobile Net V2 network model
Mobile Net V2 network model

Figure 3.

Cad system framework based on deep learning recognition
Cad system framework based on deep learning recognition

Figure 4.

Model training results
Model training results

Figure 5.

Experimental results
Experimental results

Figure 6.

Cad system comparison experimental results
Cad system comparison experimental results

Performance comparison results

Model FWIoU MPA Background FWIoU Diagnostic FWIoU
MobileNet 0.834 0.783 0.886 0.679
VGG 0.856 0.838 0.896 0.786
Resnet50 0.898 0.868 0.928 0.805
Ours 0.904 0.881 0.941 0.807

Comprehensive evaluation of image recognition

Sensitivity Specificity PPV NPV AUC F1score
Diseased 0.965 0.998 0.995 0.984 0.999 0.976
Normal 0.981 0.985 0.959 0.995 0.999 0.979
Viral Pneumonia 0.974 0.987 0.981 0.992 0.999 0.975
Mean 0.973 0.990 0.978 0.990 0.996 0.977
Average (no picture segmentation) 0.966 0.985 0.967 0.983 0.996 0.964
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English
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Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other
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