An Autoencoder based Technique for DNA Microarray Image Denoising

Mohandas, Arya; Joseph, Steffy Maria; Sathidevi, P. S.

doi:10.1109/iccsp48568.2020.9182265

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…They used plant images for their disease classification model. In [11], Mohandas et al used autoencoder as a noise reduction technique on DNA Microarray images. They compared their proposed method with some denoising methods such as Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT) based on just PSNR evaluation metric.…”

Section: Discussionmentioning

confidence: 99%

“…The parameters used during the training process of the autoencoder can be seen in Table 1. [4][5][6][7][8][9][10][11] are the used dataset, similarity metrics and parameters. This study differs from the similar studies in the literature by testing well-known traditional and deep learning-based noise removal methods from face images of well-known noises with different metrics.…”

Section: Figure 9 the Proposed Cdae Model For Image Denoisingmentioning

confidence: 99%

“…In the end, they measured the efficiency of the autoencoder as a feature extractor and denoiser. [11] used Autoencoder as a technique for denoising of DNA Microarray images (MAI). The obtaining the spot intensity of MAI is very important to diagnose some diseases, drug discovery, etc.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder

Darici

Erdem

2023

Gazi University Journal of Science

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Denoising is one of the most important preprocesses in image processing. Noises in images can prevent extracting some important information stored in images. Therefore, before some implementations such as image classification, segmentation, etc., image denoising is a necessity to obtain good results. The purpose of this study is to compare the deep learning techniques and traditional techniques on denoising facial images considering two different types of noise (Gaussian and Salt&Pepper). Gaussian, Median, and Mean filters have been specified as traditional methods. For deep learning methods, deep convolutional denoising autoencoders (CDAE) structured on three different optimizers have been proposed. Both accuracy metrics and computational times have been considered to evaluate the denoising performance of proposed autoencoders, and traditional methods. The utilized standard evaluation metrics are the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). It has been observed that overall, while the traditional methods gave results in shorter times in terms of computation times, the autoencoders performed better concerning the evaluation metrics. The CDAE based on the Adam optimizer has been shown the best results in terms of PSNR and SSIM metrics on removing both types of noise

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Section: Discussionmentioning

confidence: 99%

Section: Figure 9 the Proposed Cdae Model For Image Denoisingmentioning

confidence: 99%

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A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder

Darici

Erdem

2023

Gazi University Journal of Science

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“…Microarray analysis is a technology which enables the analysis and classification of genes in a rapid manner. At present, microarray is the main tool for gene-based investigations [1]. Microarray technique is used to monitor large number of tissue array images in a concurrent manner.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Lempel Ziv Markov Based Microarray Image Compression Algorithm

Sowmyalakshmi¹,

Waly²,

Sikkandar³

et al. 2021

Computers, Materials &Amp; Continua

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In the recent years, microarray technology gained attention for concurrent monitoring of numerous microarray images. It remains a major challenge to process, store and transmit such huge volumes of microarray images. So, image compression techniques are used in the reduction of number of bits so that it can be stored and the images can be shared easily. Various techniques have been proposed in the past with applications in different domains. The current research paper presents a novel image compression technique i.e., optimized Linde-Buzo-Gray (OLBG) with Lempel Ziv Markov Algorithm (LZMA) coding technique called OLBG-LZMA for compressing microarray images without any loss of quality. LBG model is generally used in designing a local optimal codebook for image compression. Codebook construction is treated as an optimization issue and can be resolved with the help of Grey Wolf Optimization (GWO) algorithm. Once the codebook is constructed by LBG-GWO algorithm, LZMA is employed for the compression of index table and raise its compression efficiency additionally. Experiments were performed on high resolution Tissue Microarray (TMA) image dataset of 50 prostate tissue samples collected from prostate cancer patients. The compression performance of the proposed coding esd compared with recently proposed techniques. The simulation results infer that OLBG-LZMA coding achieved a significant compression performance compared to other techniques.

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