ArticleHealth care management science2020
IoT with cloud based lung cancer diagnosis model using optimal support vector machine.
Article in Health care management science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- An optimized convolutional neural network architecture for lung cancer detection.APL bioengineering · 2024Article
- Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter.Journal of cancer research and clinical oncology · 2023Article
- Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer.Bioengineering (Basel, Switzerland) · 2023Article
- Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things.Computational intelligence and neuroscience · 2022Article
- Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques.BioMed research international · 2022Article
- Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review.Journal of medical Internet research · 2020Article
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Authors and funding
2 authors.
Funding
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Abstract
In the last decade, exponential growth of Internet of Things (IoT) and cloud computing takes the healthcare services to the next level. At the same time, lung cancer is identified as a dangerous disease which increases the global mortality rate annually. Presently, support vector machine (SVM) is the effective image classification tool especially in medical imaging. Feature selection and parameter optimization are the effective ways to improve the results of SVM and are conventionally resolved individually. This paper presents an optimal SVM for lung image classification where the parameters of SVM are optimized and feature selection takes place by modified grey wolf optimization algorithm combined with genetic algorithm (GWO-GA). The experimentation part takes place on three dimensions: test for parameter optimization, feature selection, and optimal SVM. For assessing the performance of the presented approach, a benchmark image database is employed which comprises of 50 low-dosage and stored lung CT images. The presented method exhibits its superior results on all the applied test images under several aspects. In addition, it achieves average classification accuracy of 93.54 which is significantly higher than the compared methods.
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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.