Deep Learning based Anomaly Detection in Images: Insights, Challenges and Recommendations

Alloqmani, Ahad; Yoosef, B; Irshad, Asif; Alsolami, Fawaz

doi:10.14569/ijacsa.2021.0120428

Cited by 18 publications

(10 citation statements)

References 24 publications

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“…Based on the patient's medical history, predicted diseases are employed in conjunction with this technique. We will utilize this approach once again to get information about the condition of our family members [24][25][26][27][28][29][30][31][32][33].…”

Section: Methodsmentioning

confidence: 99%

Analysis of the Exploration of Security and Privacy for Healthcare Management Using Artificial Intelligence: Saudi Hospitals

Almalawi

Khan

Alsolami

et al. 2022

Computational Intelligence and Neuroscience

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A large component of the Health Information Systems now comprises numerous independent apps created in the past that need to be merged to provide a more uniform service. In addition to affecting the Intelligent Health Board Functionality and dependability, the quality of these additional apps may also have an impact. A critical characteristic of the SHS's management and upkeep is the SHS's reliance on the real benefits provided to it. In speaking, an HMIS (Healthcare Management Information System) is a computer-based device that benefits medical practitioners to perform their duties more efficiently by coordinating all of their data. Even though these systems are widely used by most of the world, there is a significant need to comprehend these technologies and indeed the potential they provide. Healthcare data warehouses in Saudi Arabia have evolved through time, and this research examines how key service improvements in Saudi present varied viewpoints on how premium initiative help may be attained in health as well as how this could be done. When it comes to understanding how different types of medical professionals interact with healthcare systems throughout history, researchers developed stages of the maturity model.

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Section: Methodsmentioning

confidence: 99%

Analysis of the Exploration of Security and Privacy for Healthcare Management Using Artificial Intelligence: Saudi Hospitals

Almalawi

Khan

Alsolami

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

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“…After the VAG model reconstructs the images x corresponding to the normal state of the input images, use the pixel-wise anomaly detection algorithm to calculate the pixel-level anomaly residual score Ars(i, j) between x and x. In this paper, we leverage the cosine distance [40] as the anomaly residual score which is defined in Equation (22), to calculate the difference for each pixel of both images.…”

Section: Online Anomaly Detectingmentioning

confidence: 99%

“…Anomaly detection methods usually use available normal data to extract, characterize and model the patterns, and then develop reasonable anomaly detectors to discover new or abnormal patterns in the newly observed data [19][20][21]. Due to being scarce and difficult to gather enough abnormal samples, unsupervised learning methods are more effective to use in most practical application scenarios [22,23]. Surveys on anomaly detection in power transformers were conducted as follows.…”

Section: Introductionmentioning

confidence: 99%

Application of Thermography and Adversarial Reconstruction Anomaly Detection in Power Cast-Resin Transformer

Fanchiang

Kuo

2022

Sensors

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Dry-type power transformers play a critical role in the power system. Detecting various overheating faults in the running state of the power transformer is necessary to avoid the collapse of the power system. In this paper, we propose a novel deep variational autoencoder-based anomaly detection method to recognize the overheating position in the operation of the dry-type transformer. Firstly, the thermal images of the transformer are acquired by the thermal camera and collected for training and testing datasets. Next, the variational autoencoder-based generative adversarial networks are trained to generate the normal images with different running conditions from heavy to light loading. Through the pixel-wise cosine difference between original and reconstructed images, the residual images with faulty features are obtained. Finally, we evaluate the trained model and anomaly detection method on normal and abnormal testing images to demonstrate the effeteness and performance of the proposed work. The results show that our method effectively improves the anomaly accuracy, AUROC, F1-scores and average precision, which is more effective than other anomaly detection methods. The proposed method is simple, lightweight and has less storage size. It reveals great advantages for practical applications.

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“…Compare the anomaly detection performance of DUAL-ADGAN with the other nine unsupervised anomaly detection baseline models on three datasets, RealAdExchange-CPC, RealTraffic-SPEED, and Real-Traffic-TravelTime. (3) For epochs do (4) Feed the noise vector z into the generator G W to generate the data G W (5) Feed the generated data G W (z) and the real data x into the discriminator D W (6) Training G W and D W with WGAN-GP loss function (7) Return G W (8) If model is Fence-GAN: (9) For epochs do (10) Feed the noise vector z into the generator G F to generate the data G F (z) (11) Feed the generated data G F (z) and the real data x into the discriminator D F (12) Training G F and D F with Fence-GAN loss function (13) Step 2.…”

Section: Experimental Protocolmentioning

confidence: 99%

Research on DUAL-ADGAN Model for Anomaly Detection Method in Time-Series Data

Gong

Wang

2022

Computational Intelligence and Neuroscience

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In recent years, anomaly detection techniques in time-series data have been widely used in manufacturing, cybersecurity, and other fields. Meanwhile, various anomaly detection models based on generative adversarial networks (GAN) are gradually used in time-series anomaly detection tasks. However, there are problems of unstable generator training, missed detection of anomalous data, and inconsistency between the discriminator’s discriminant and the anomaly detection target in GAN networks. Aiming at the above problems, the paper proposes a DUAL-ADGAN (Dual Anomaly Detection Generative Adversarial Networks) model for the detection of anomalous data in time series. First, the Wasserstein distance satisfying the Lipschitz constraint is used as the loss function of the data reconstruction module, which improves the stability of the traditional GAN network training. Second, by adding a data prediction module to the DUAL-ADGAN model, the distinction between abnormal and normal samples is increased, and the rate of missing abnormal data in the model is reduced. Third, by introducing the Fence-GAN loss function, the discriminator is aligned with the anomaly detection target, which effectively reduces the anomaly data false detection rate of the DUAL-ADGAN model. Finally, anomaly scores derived from the DUAL-ADGAN model are compared with dynamic thresholds to detect anomalies. The experimental results show that the average F1 of the DUAL-ADGAN model is 0.881, which is better than the other nine baseline models. The conclusions demonstrate that the DUAL-ADGAN model proposed in the paper is more stable in training while effectively solving the problems of anomaly miss detection and discriminator inconsistency with the anomaly detection target in the anomaly detection task.

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Deep Learning based Anomaly Detection in Images: Insights, Challenges and Recommendations

Cited by 18 publications

References 24 publications

Analysis of the Exploration of Security and Privacy for Healthcare Management Using Artificial Intelligence: Saudi Hospitals

Analysis of the Exploration of Security and Privacy for Healthcare Management Using Artificial Intelligence: Saudi Hospitals

Application of Thermography and Adversarial Reconstruction Anomaly Detection in Power Cast-Resin Transformer

Research on DUAL-ADGAN Model for Anomaly Detection Method in Time-Series Data

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