RDH-based dynamic weighted histogram equalization using for secure transmission and cancer prediction

Abbasi, Rashid; Chen, Jianwen; Al‐Otaibi, Yasser D.; Rehman, Amjad; Abbas, Asad; Cui, Wei

doi:10.1007/s00530-020-00718-w

Cited by 14 publications

(6 citation statements)

References 52 publications

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“…Due to system noise, underexposure (or overexposure), relative motion, and other factors, the acquired image often has some differences (called degradation or degradation) from the original image. Image enhancement technology [24] is one of the most basic objects of digital image processing research. e main goal of image enhancement is to make the image highlight some aspect of the information in the image that is relevant to current needs while weakening or removing some unnecessary information.…”

Section: Grayscale Transformation Methodmentioning

confidence: 99%

Image Contrast Enhancement Method Based on Nonlinear Space and Space Constraints

Zhao¹,

Xia²

2022

Wireless Communications and Mobile Computing

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Remote sensing, environmental monitoring, pattern recognition, and other fields all use this critical and indispensable basic technology. The article proposes an image contrast enhancement detection algorithm based on a linear model to address the problem that the current global contrast enhancement detection algorithm does not have high classification accuracy under the low-intensity JPEG compression quality factor. To decompose the original image, use the biorthogonal wavelet transform. The improved fuzzy set enhancement algorithm is used for the low-frequency subband coefficient. The results obtained after simulating this algorithm show that it is very effective in improving contrast, enhancing image details, and suppressing noise. It has the ability to greatly improve the image’s visual effect, as well as the advantages of parameter adaptation and high algorithm efficiency.

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Section: Grayscale Transformation Methodmentioning

confidence: 99%

Image Contrast Enhancement Method Based on Nonlinear Space and Space Constraints

Zhao¹,

Xia²

2022

Wireless Communications and Mobile Computing

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“…It's meant to draw attention to details hidden by the image's limited dynamic range. Most commonly, Histogram Equalization (HE) is applied to improve image contrast [25]. The input images' contrast is improved as a result of the pixels' values being expanded to encompass the entire histogram [26,27].…”

Section: Contrast Enhancementmentioning

confidence: 99%

Breast Cancer Classification based on Artificial Intelligence: State of the Art

Shaban

2023

Nile Journal of Communication and Computer Science

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Breast cancer, a formidable ailment, stands as a prominent contributor to global female mortality rates. The timely detection of breast cancer is of utmost importance as it significantly enhances the probability of a favourable prognosis while concurrently reducing the likelihood of the disease advancing to an incurable state. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as prominent methodologies for the precise detection and classification of breast cancer within Computer-Aided Diagnosis (CAD) systems. This paper provides a comprehensive overview of the existing body of literature pertaining to the application AI in the realm of breast cancer detection. The primary objective of this study is to underscore the significance of employing AI in the timely identification of breast cancer, thereby enhancing the efficacy of subsequent treatment interventions. Furthermore, an examination of different screening methodologies for the detection of breast cancer is presented. Furthermore, we explore the fundamental components of CAD system, including preprocessing, segmentation, feature extraction, and feature selection. This paper will extensively examine the various classification strategies employed in the identification of breast cancer.

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“…Recent CNN-based works have allowed for DNA sequence training rather than preliminary feature extraction. RNN connections can generate a directory graph in a sequence, allowing RNNs to extract features from DNA sequences in a novel and efficient way [52][53][54][55][56][57][58][59][60].…”

Section: Dl-ac4cmentioning

confidence: 99%

Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep Learning

et al. 2022

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Deep learning models have been successfully applied in a wide range of fields. The creation of a deep learning framework for analyzing high-performance sequence data have piqued the research community’s interest. N4 acetylcytidine (ac4C) is a post-transcriptional modification in mRNA, is an mRNA component that plays an important role in mRNA stability control and translation. The ac4C method of mRNA changes is still not simple, time consuming, or cost effective for conventional laboratory experiments. As a result, we developed DL-ac4C, a CNN-based deep learning model for ac4C recognition. In the alternative scenario, the model families are well-suited to working in large datasets with a large number of available samples, especially in biological domains. In this study, the DL-ac4C method (deep learning) is compared to non-deep learning (machine learning) methods, regression, and support vector machine. The results show that DL-ac4C is more advanced than previously used approaches. The proposed model improves the accuracy recall area by 9.6 percent and 9.8 percent, respectively, for cross-validation and independent tests. More nuanced methods of incorporating prior bio-logical knowledge into the estimation procedure of deep learning models are required to achieve better results in terms of predictive efficiency and cost-effectiveness. Based on an experiment’s acetylated dataset, the DL-ac4C sequence-based predictor for acetylation sites in mRNA can predict whether query sequences have potential acetylation motifs.

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RDH-based dynamic weighted histogram equalization using for secure transmission and cancer prediction

Cited by 14 publications

References 52 publications

Image Contrast Enhancement Method Based on Nonlinear Space and Space Constraints

Image Contrast Enhancement Method Based on Nonlinear Space and Space Constraints

Breast Cancer Classification based on Artificial Intelligence: State of the Art

Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep Learning

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