ReviewJournal of healthcare engineering2021
Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms.
Review in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- Smart Nanomedicines for Neurodegenerative Diseases: Empowering New Therapies with Molecular Imaging and Artificial Intelligence.Molecular diagnosis & therapy · 2026Review
- Pattern and structural detection in grayscale images through the application of quantile graphs in higher-dimensional spaces.Scientific reports · 2025Article
- Deep learning-based characterization of neutrophil activation phenotypes inComputational and structural biotechnology journal · 2024Article
- Article
- Evaluating the Accuracy of AI-Based Software vs Human Interpretation in the Diagnosis of Dental Caries Using Intraoral Radiographs: An RCT.Journal of pharmacy & bioallied sciences · 2024Article
- Early detection of Alzheimer's disease in structural and functional MRI.Frontiers in medicine · 2024Article
- A big data analysis algorithm for massive sensor medical images.PeerJ. Computer science · 2024Article
- Article
- Using Mechanistic Models and Machine Learning to Design Single-Color Multiplexed Nascent Chain Tracking Experiments.bioRxiv : the preprint server for biology · 2023Article
- Using mechanistic models and machine learning to design single-color multiplexed nascent chain tracking experiments.Frontiers in cell and developmental biology · 2023Article
- Combined segmentation and classification-based approach to automated analysis of biomedical signals obtained from calcium imaging.PloS one · 2023Article
- The Impact of Tax Reduction and Fee Reduction Based on Big Data Algorithm on the High-Quality Development of the Real Economy under the Action of Coupling Effect or Substitution Effect.Computational intelligence and neuroscience · 2022Article
- The Application of the Big Data Medical Imaging System in Improving the Medical and Health Examination.Journal of healthcare engineering · 2021Article
- Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis.Journal of healthcare engineering · 2021Review
- An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection.Journal of healthcare engineering · 2021Review
- Prediction of Multidrug-Resistant Tuberculosis Using Machine Learning Algorithms in SWAT, Pakistan.Journal of healthcare engineering · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In modern-day medicine, medical imaging has undergone immense advancements and can capture several biomedical images from patients. In the wake of this, to assist medical specialists, these images can be used and trained in an intelligent system in order to aid the determination of the different diseases that can be identified from analyzing these images. Classification plays an important role in this regard; it enhances the grouping of these images into categories of diseases and optimizes the next step of a computer-aided diagnosis system. The concept of classification in machine learning deals with the problem of identifying to which set of categories a new population belongs. When category membership is known, the classification is done on the basis of a training set of data containing observations. The goal of this paper is to perform a survey of classification algorithms for biomedical images. The paper then describes how these algorithms can be applied to a big data architecture by using the Spark framework. This paper further proposes the classification workflow based on the observed optimal algorithms, Support Vector Machine and Deep Learning as drawn from the literature. The algorithm for the feature extraction step during the classification process is presented and can be customized in all other steps of the proposed classification workflow.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.