A review: The detection of cancer cells in histopathology based on machine vision

He, Wenbin; Liu, Ting; Han, Yongjie; Ming, Wuyi; Du, Jinguang; Liu, Yinxia; Yang, Yuan; Wang, Leijie; Jiang, Zhiwen; Wang, Yongqiang; Yuan, Jie; Cao, Chen

doi:10.1016/j.compbiomed.2022.105636

Cited by 29 publications

(11 citation statements)

References 123 publications

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“…The age-standardized death rate in these high-burden states was around 6/1,00,000, with half of all deaths occurring between June and September. Russell's vipers were responsible for the majority of envenomations (43.2%), followed by kraits (17.7%) and cobras (13.7%) [24].…”

Section: Methodsmentioning

confidence: 99%

Health evaluation and dangerous reptile detection using a novel framework powered by the YOLO algorithm to design high‐content cellular imaging systems

Pandey,

Kumar,

Yadav

et al. 2023

The Journal of Engineering

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This novel approach in animal biology could revolutionize identifying endangered species, addressing the issue of misclassifying potentially harmful animals based solely on visual characteristics. Particularly impactful for farmers in agricultural fields, it aims to reduce the heightened risk of venomous animal attacks, ultimately improving safety. Due to a lack of accessible education, illiterate farmers are more susceptible to adopting superstitious beliefs, which tragically leads to fatal snakebites even when medical treatment is readily available. Furthermore, environmental factors can unexpectedly hold typically non‐threatening animals responsible for a large number of human deaths each year. However, the complexity of human recognition of these hazards has prompted the development of a novel design approach aimed at simplifying the process. Integration of the ResNet learning algorithm in conjunction with You Only Look Once (YOLOv5) within the framework is recommended to facilitate real‐time processing and improve accuracy. This combined approach not only speeds up animal recognition but also takes advantage of ResNet's deep learning capabilities. The first phase entails deploying YOLOv5 to detect the presence of snakes in the proposed study, achieving a remarkable 87% precision in snake detection thanks to the synergistic fusion of ResNet and YOLOv5.

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Section: Methodsmentioning

confidence: 99%

Health evaluation and dangerous reptile detection using a novel framework powered by the YOLO algorithm to design high‐content cellular imaging systems

Pandey,

Kumar,

Yadav

et al. 2023

The Journal of Engineering

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“…These algorithms will be improved as they are applied, and it is believed that future artificial intelligence will also produce more new algorithms that the optimization algorithm will apply to the EDM, such as selfish herds optimization, bald eagle search, etc. (4) With the fast advancement of technology, machine learning (ML) has found widespread use in a variety of industries, including industrial testing [121][122][123], medical diagnostics [124,125], life sciences [126,127], and renewable energy [128][129][130]. AlphaFold2, for instance, created a protein structure prediction model using ML, which can predict the properties of proteins based on gene sequences and achieve 98.5% of the structure of human proteins [126].…”

Section: Outlooksmentioning

confidence: 99%

Parameters Optimization of Electrical Discharge Machining Process Using Swarm Intelligence: A Review

Chen

et al. 2023

Metals

Self Cite

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Electrical discharge machining (EDM) can use soft tool electrodes to process hard workpieces to achieve “soft against hard”, because it directly uses electrical energy and thermal energy to remove metal materials. Then, it can generate complex features on harder materials and meet the requirements of excellent surface quality. Since EDM involves many process parameters, including electrical parameters, non-electrical parameters, and materials properties, it is essential to optimize its process parameters to obtain good performance. In this direction, the application of the swarm intelligence (SI) technique has become popular. In this paper, the existing literature is comprehensively reviewed, and the application of the SI technique in the optimization of EDM process parameters is summarized. Sinker-EDM (SEDM), wire-EDM (WEDM), and micro-EDM (MEDM) with various hybrid techniques are among the EDM methods considered in this study because of their broad adoption in industrial sections. The fundamental nature of all review articles will assist engineers/workers in determining the process parameters and processing performance, the SI algorithm, and the optimal technique by which to obtain the desired process parameters. In addition, discussions from the perspectives of the similarity, individuality, and complementarity of various SI algorithms are proposed, and necessary outlooks are predicted, which provides references for the high performance of the EDM processes in the future.

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“…The cell-level algorithms are designed to analyze individual cells and their features from WSIs or patches that have been extracted from WSIs. Examples include cell segmentation, detection, classification, and mitosis detection [19][20][21][22][23][24][25][26][27]. Such methods can assist pathologists in identifying any irregularities in the cellular landscape that might be indicative of the severity of disease and patient prognosis.…”

Section: The Promise Of Ai In Computational Pathologymentioning

confidence: 99%

Unleashing the potential of AI for pathology: challenges and recommendations

Asif

Rajpoot

Graham

et al. 2023

The Journal of Pathology

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Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer‐reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre‐clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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A review: The detection of cancer cells in histopathology based on machine vision

Cited by 29 publications

References 123 publications

Health evaluation and dangerous reptile detection using a novel framework powered by the YOLO algorithm to design high‐content cellular imaging systems

Health evaluation and dangerous reptile detection using a novel framework powered by the YOLO algorithm to design high‐content cellular imaging systems

Parameters Optimization of Electrical Discharge Machining Process Using Swarm Intelligence: A Review

Unleashing the potential of AI for pathology: challenges and recommendations

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