Traditional Model vs Proposed Model(Without GAN).

<p>From the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0324180#pone.0324180.s002" target="_blank">S2 Fig</a>, it is evident that the suggested model (without using GAN) shows significant improvements across all metrics compared to trad...

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Bibliographic Details
Main Author:	Jian Deng (681236) (author)
Other Authors:	Zheng Cheng (426625) (author), Aiming Gu (21433294) (author), Shibohua Zhang (21433297) (author)
Published:	2025
Subjects:	Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified traditional diagnostic approaches reduce maintenance costs prolonging service life medical imaging services informed neural networks full lifecycle management experimental results show especially making breakthroughs fault diagnosis plays paper puts forward guaranteeing stable operation proposed model outperforms model &# 8217 medical imaging equipment improve equipment availability leveraging inverse pinn inverse pinn ). fault detection accuracy predict potential faults inverse pinn fault detection equipment operation paper discusses inverse physics physical model xlink "> vital role practical significance overall efficiency imbalanced datasets imbalanced data engineering aspects data imbalance data generation accurately identify
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Traditional Model vs Proposed Model(Without GAN).

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