SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM Images

Taha, Bakr Ahmed; Al-Jumaily, Abdulmajeed; Mashhadany, Yousif Al; Zan, Mohd Saiful Dzulkefly; Arsad, Norhana

doi:10.3390/v14112386

Cited by 14 publications

(6 citation statements)

References 68 publications

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“…Beyond segmentation, the RNN has been used for prediction as well as object tracking. For example, Taha et al [176] trained an RNN to detect the real virus levels from the images generated by TEM. Their test data is a SARS-CoV-2 dataset which has 519 images from patients with COVID-19 in Italy.…”

Section: Applications Of Rnnsmentioning

confidence: 99%

Advancing electron microscopy using deep learning

Chen,

Barnard

2024

J. Phys. Mater.

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Electron microscopy, a sub-field of microanalysis, is a victim of its own success. The widespread use of electron microscopy for imaging molecules and materials has had an enormous impact on our understanding of countless systems and has accelerated impacts in drug discovery and materials design, for electronic, energy, environment and health applications. With this success a bottleneck has emerged, as the rate at which we can collect data has significantly exceeded the rate at which we can analyse it. Fortunately, this has coincided with the rise of advanced computational methods, including data science and machine learning. Deep learning, a sub-field of machine learning capable of learning from large quantities of data such as images, is ideally suited to overcome some of the challenges of electron microscopy at scale. There are a variety of different deep learning approaches relevant to the field, with unique advantages and disadvantages. In this review, we describe some well-established methods, with some recent examples, and introduce some new methods currently emerging in computer science. Our summary of deep learning is designed to guide electron microscopists to choose the right deep learning algorithm for their research and prepare for their digital future.

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Section: Applications Of Rnnsmentioning

confidence: 99%

Advancing electron microscopy using deep learning

Chen,

Barnard

2024

J. Phys. Mater.

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“…Kudyshev et al investigated the novel use of physically informed ML in quantum photonics to overcome these obstacles and advance the field. [11][12][13][14][15][16] ML techniques are expected to help in the rapid selection of optimal single photon sources through guided quantum measurements. ML frameworks also facilitate the development of efficient quantum circuit elements and novel deterministic assembly techniques for on-chip quantum devices.…”

Section: Introductionmentioning

confidence: 99%

Exploring Trends and Opportunities in Quantum‐Enhanced Advanced Photonic Illumination Technologies

Taha,

Addie,

Haider

et al. 2024

Adv Quantum Tech

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The development of quantum‐enabled photonic technologies has opened new avenues for advanced illumination across diverse fields, including sensing, computing, materials, and integration. This review highlights how Quantum‐enhanced sensing and imaging exploit nonclassical correlations to attain unprecedented accuracy in chaotic environments. As well as guaranteeing secure communications, quantum cryptography, protected by physical principles, ensures unbreakable cryptographic key exchange. As quantum computing speed increases exponentially, previously unimplementable uses for classical computers become feasible. On‐chip integration enables the mass production of quantum photonic components for pervasive applications by facilitating miniaturization and scalability. A powerful and flexible platform is produced when classical and quantum systems are combined. Quantum spin liquids and other topological materials can maintain their quantum states while subject to decoherence. Despite challenges with decoherence, production, and commercialization, quantum photonics is an exciting new area of study that promises lighting techniques impossible with conventional optics. To realize this promise, researchers from several fields must work together to solve complex technical problems and decode fundamental physics. Finally, advances in quantum‐enabled photonics have the potential to evolve quantum photonic devices and cutting‐edge imaging methods and usher in a new age of lighting options based on quantum dots.

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“…21 Initiatives such as connected and smart healthcare hold promise for the future. [22][23][24][25][26] Being able to automate the scoring of the Comet images using Deep Learning can enable them. Automating the quantification of comet images can enable scoring on the edge.…”

mentioning

confidence: 99%

Automated Quantification of DNA Damage Using Deep Learning and Use of Synthetic Data Generated from Basic Geometric Shapes

Namuduri,

Mehta,

Barbe

et al. 2024

ECS Sens. Plus

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Comet assays are used to assess the extent of Deoxyribonucleic acid (DNA) damage, in human cells, caused by substances such as novel drugs or nano materials. Deep learning is showing promising results in automating the process of quantifying the percentage of damage using the assay images. But the lack of large datasets and imbalanced data is a challenge. In this study, synthetic comet assay images generated from simple geometric shapes were used to augment the data for training the Convolutional Neural Network. The results from the model trained using the augmented data were compared with the results from a model trained exclusively on real images. It was observed that the use of synthetic data in training not only gave a significantly better coefficient of determination (R2), but also resulted in a more robust model i.e., with less variation in R2 compared to training without synthetic data. This approach can lead to improved training while using a smaller training dataset, saving cost and effort involved in capturing additional experimental images and annotating them. Additional benefits include addressing imbalanced datasets, and data privacy concerns. Similar approaches must be explored in other low data domains to extract the same benefits.

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SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM Images

Cited by 14 publications

References 68 publications

Advancing electron microscopy using deep learning

Advancing electron microscopy using deep learning

Exploring Trends and Opportunities in Quantum‐Enhanced Advanced Photonic Illumination Technologies

Automated Quantification of DNA Damage Using Deep Learning and Use of Synthetic Data Generated from Basic Geometric Shapes

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