Binary Particle Swarm Optimization Intelligent Feature Optimization Algorithm‐Based Magnetic Resonance Image in the Diagnosis of Adrenal Tumor

Xu, Jian; Tian, Fei; Wang, Lei; Zhongchang, Miao

doi:10.1155/2022/5143757

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…These patients were randomly divided into two groups, each with 60 patients: the control group, which was examined using conventional MRI, and the observation group, which was examined using MRI in conjunction with BPSO, with the second group achieving an accuracy of 81.67 percent and the first group achieving an accuracy of 58.33 percent. They pointed out that the results were not sufficient, and the relationship between the site of the disease and benign and malignant tumours was not studied as a future direction for research [23].…”

Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

خوارزميات ذکاء السرب لتحسين في تشخيص الأمراض: مسح

علي,

امام,

عزام

2023

النشرة المعلوماتية في الحاسبات والمعلومات

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For quick diagnosis, treatment, and healing, many diseases need to be caught early. Delays in diagnosis can lead to other risks. Recently, researchers have been using artificial intelligence to find many diseases quickly and accurately. In particular, they have been using machine learning, CNN, and optimisation algorithms to pick the right features for a simple training model for the classification stage. As most data sets have noisy and repetitive features in all application areas, this slows down the performance of the classifier and may even make the classification less accurate because the search space is so big. This also affects the runtime of the classification. This review gives a full look at Particle Swarm Optimisation (PSO), Artificial Bee Colony (ABC), and Grey Wolf Optimisation (GWO). It will also talk about how these methods can be used to diagnose diseases like skin cancer, adrenal gland tumours, diabetes, coronary heart disease, and others. Also, the different procedures that researchers have taken to improve the accuracy and speed of diagnosis, the changes they have made to these algorithms, hybrids of these algorithms, and proposed future trends in every search The base of this study is to help new researchers get an overview of swarm intelligence algorithms and their role in diagnosing diseases and to brighten their horizons for the future directions in this field.

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Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

خوارزميات ذکاء السرب لتحسين في تشخيص الأمراض: مسح

علي,

امام,

عزام

2023

النشرة المعلوماتية في الحاسبات والمعلومات

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Independent vector analysis based on binary grey wolf feature selection and extreme learning machine for bearing fault diagnosis

2022

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Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

Martindale,

Llewellyn,

de Visser

et al. 2024

Nat Commun

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The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77–94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.

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Binary Particle Swarm Optimization Intelligent Feature Optimization Algorithm‐Based Magnetic Resonance Image in the Diagnosis of Adrenal Tumor

Cited by 3 publications

References 28 publications

خوارزميات ذکاء السرب لتحسين في تشخيص الأمراض: مسح

خوارزميات ذکاء السرب لتحسين في تشخيص الأمراض: مسح

Independent vector analysis based on binary grey wolf feature selection and extreme learning machine for bearing fault diagnosis

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

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