Dynamic weighted histogram equalization for contrast enhancement using for Cancer Progression Detection in medical imaging

Abbasi, Rashid; Xu, Lixiang; Wang, Zheng; Chughtai, Gohar Rehman; Amin, Farhan; Luo, Bin

doi:10.1145/3297067.3297086

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…The high drug-treated count is critical in medical industries; when medical organisations produce some new medicines, first there is a need to test the effect of newly created treatment, essential to measure how much it can damage the normal cell, so the measurement of performance in high drug-treated detection task is based on the number of correctly detected high drug-treated cells, rather than the shape of detected high drug-treated cells. Similarly, in the diseased, for identification of diseased (clinical medicine, radiology, pathology, and cancer) [ 59 – 61 ], also measurement of performance is based on the diseased detection task, and correct detection of diseased cells, not the shape. The correct detection criteria of drug and diseased cells are a distance from the centroid of ground truth drug and diseased cells.…”

Section: Resultsmentioning

confidence: 99%

Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network

Nawabi

Sheng

Abbasi

et al. 2022

Oxidative Medicine and Cellular Longevity

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Most multicellular organisms require apoptosis, or programmed cell death, to function properly and survive. On the other hand, morphological and biochemical characteristics of apoptosis have remained remarkably consistent throughout evolution. Apoptosis is thought to have at least three functionally distinct phases: induction, effector, and execution. Recent studies have revealed that reactive oxygen species (ROS) and the oxidative stress could play an essential role in apoptosis. Advanced microscopic imaging techniques allow biologists to acquire an extensive amount of cell images within a matter of minutes which rule out the manual analysis of image data acquisition. The segmentation of cell images is often considered the cornerstone and central problem for image analysis. Currently, the issue of segmentation of mitochondrial cell images via deep learning receives increasing attention. The manual labeling of cell images is time-consuming and challenging to train a pro. As a courtesy method, mitochondrial cell imaging (MCI) is proposed to identify the normal, drug-treated, and diseased cells. Furthermore, cell movement (fission and fusion) is measured to evaluate disease risk. The newly proposed drug-treated, normal, and diseased image segmentation (DNDIS) algorithm can quickly segment mitochondrial cell images without supervision and further segment the highly drug-treated cells in the picture, i.e., normal, diseased, and drug-treated cells. The proposed method is based on the ResNet-50 deep learning algorithm. The dataset consists of 414 images mainly categorised into different sets (drug, diseased, and normal) used microscopically. The proposed automated segmentation method has outperformed and secured high precision (90%, 92%, and 94%); moreover, it also achieves proper training. This study will benefit medicines and diseased cell measurements in medical tests and clinical practices.

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Section: Resultsmentioning

confidence: 99%

Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network

Nawabi

Sheng

Abbasi

et al. 2022

Oxidative Medicine and Cellular Longevity

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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

et al. 2021

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Dynamic weighted histogram equalization for contrast enhancement using for Cancer Progression Detection in medical imaging

Cited by 6 publications

References 20 publications

Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network

Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network

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

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

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