Denoising Convolutional Variational Autoencoders-Based Feature Learning for Automatic Detection of Plant Diseases

Zilvan, Vicky; Ramdan, Ade; Suryawati, Endang; Kusumo, R. Budiarianto Suryo; Krisnandi, Dikdik; Pardede, Hilman F.

doi:10.1109/icicos48119.2019.8982494

Cited by 15 publications

(13 citation statements)

References 14 publications

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“…This runs against the traditional applications of VAE that some researchers focused on data generation [15]- [17]. Besides, denoising based on VAE is proposed in [18] while Anh et al [19] introduce VAE as a feature extraction to combine with random forest for fraud detection.…”

Section: U-net With Variational Data Imputationmentioning

confidence: 99%

Improving RepVGG model with variational data imputation in COVID-19 classification

Trang

Nguyen

TonThat

et al. 2022

IJ-AI

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<span>Millions of fatal cases have been reported worldwide as a result of the Coronavirus disease 2019 (COVID-19) outbreak. In order to stop the spreading of disease, early diagnosis and quarantine of infected people are one of the most essential steps. Therefore, due to the similar symptoms of SARS-CoV-2 virus and other pneumonia, identifying COVID-19 still exists some challenges. Reverse transcription-polymerase chain reaction (RT-PCR) is known as a standard method for the COVID-19 diagnosis process. Due to the shortage of RT-PCR toolkit in global, Chest X-Ray (CXR) image is introduced as an initial step to support patient’s classification. Applying deep learning in medical imaging becomes an advanced research trend in many applications. In this research, RepVGG pre-trained model is considered to be used as the main backbone of the network. Besides, variational autoencoder (VAE) is firstly trained to perform lung segmentation. Afterwards, the encoder part in VAE is preserved as an additional feature extractor to combine with RepVGG performing classification. A COVID-19 radiography database consisting of 3 classes COVID-19, Normal and Viral Pneumonia is conducted. The obtained average accuracy of the proposed model is 95.4% and other evaluation metrics also show better results compared with the original RepVGG model.</span>

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Section: U-net With Variational Data Imputationmentioning

confidence: 99%

Improving RepVGG model with variational data imputation in COVID-19 classification

Trang

Nguyen

TonThat

et al. 2022

IJ-AI

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“…VAEs have also succeeded in biological image analyses, and many studies show superior performance. The main research area based on the VAE use in medical imaging datasets includes: 1) Medical image data augmentation for downstream tasks include image classification [68,71,79,80], image segmentation [72][73][74][75][76]87], image restoration [85,86], and image reconstruction [72,[81][82][83].…”

Section: Medical Imaging and Image Analysesmentioning

confidence: 99%

“…It cannot only produce diverse results, but can also be leveraged for downstream processing. Zilvan et al [85] proposed denoising convolutional VAE as feature extractor and also as a denoiser for disease detection tasks.…”

Section: ) Medical Image Augmentation For Down-stream Tasksmentioning

confidence: 99%

“…In addition, the VAEs can be used as a feature dimensionality reduction technique. The main benefit of feature [84] Data compression with image fidelity Reconstruction ADNI Zilvan et al 2019 [85] Utilize denoising convolutional VAE as feature extractor and also as denoiser for disease detection tasks dimensionality reduction is to eliminate any redundant features and noise, which can improve the accuracy of prediction or generalization ability and support the interpretability of research results. Although the VAE model in sequence data set analysis has challenges, such as lack of interpretability and increased potential for overfitting, VAE may be increasingly important in the high-throughput design and biological sequence annotation.…”

Section: Conclusion and Future Opportunitiesmentioning

confidence: 99%

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Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A Survey

Wei

Mahmood

2021

IEEE Access

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“…Many studies have been published on CVAE including medical applications to predict post-trauma health outcomes [ 10 ]. Another study was done by the authors of [ 11 ], which included using CVAE to automatically detect plant diseases, as well, the authors in [ 12 ] developed CVAE based system for electrocardiographic imaging (ECGI).…”

Section: Related Workmentioning

confidence: 99%

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Al-Mohannadi

Al-Maadeed

Elharrouss

et al. 2021

Sensors

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Cardiovascular diseases (CVDs) have shown a huge impact on the number of deaths in the world. Thus, common carotid artery (CCA) segmentation and intima-media thickness (IMT) measurements have been significantly implemented to perform early diagnosis of CVDs by analyzing IMT features. Using computer vision algorithms on CCA images is not widely used for this type of diagnosis, due to the complexity and the lack of dataset to do it. The advancement of deep learning techniques has made accurate early diagnosis from images possible. In this paper, a deep-learning-based approach is proposed to apply semantic segmentation for intima-media complex (IMC) and to calculate the cIMT measurement. In order to overcome the lack of large-scale datasets, an encoder-decoder-based model is proposed using multi-image inputs that can help achieve good learning for the model using different features. The obtained results were evaluated using different image segmentation metrics which demonstrate the effectiveness of the proposed architecture. In addition, IMT thickness is computed, and the experiment showed that the proposed model is robust and fully automated compared to the state-of-the-art work.

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Denoising Convolutional Variational Autoencoders-Based Feature Learning for Automatic Detection of Plant Diseases

Cited by 15 publications

References 14 publications

Improving RepVGG model with variational data imputation in COVID-19 classification

Improving RepVGG model with variational data imputation in COVID-19 classification

Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A Survey

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

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