Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning.
Journal
Computational and mathematical methods in medicine
ISSN: 1748-6718
Titre abrégé: Comput Math Methods Med
Pays: United States
ID NLM: 101277751
Informations de publication
Date de publication:
2022
2022
Historique:
received:
08
01
2022
revised:
08
02
2022
accepted:
09
02
2022
entrez:
29
3
2022
pubmed:
30
3
2022
medline:
5
4
2022
Statut:
epublish
Résumé
Deep learning technology has recently played an important role in image, language processing, and feature extraction. In the past disease diagnosis, most medical staff fixed the images together for observation and then combined with their own work experience to judge. The diagnosis results are subjective, time-consuming, and inefficient. In order to improve the efficiency of diagnosis, this paper applies the deep learning algorithm to the online diagnosis and classification of CT images. Based on this, in this paper, the deep learning algorithm is applied to CT image online diagnosis and classification. Based on a brief analysis of the current situation of CT image classification, this paper proposes to use the Internet of things technology to collect CT image information and establishes the Internet of things to collect the CT image model. In view of image classification and diagnosis, the convolution neural network algorithm in the deep learning algorithm is proposed to diagnose and classify CT images, and several factors affecting the accuracy of classification are proposed, including the convolution number and network layer number. Using the CT image of the hospital brain for simulation analysis, the simulation results confirm the effectiveness of the deep learning algorithm. With the increase of convolution and network layer and the decrease of compensation, the accuracy of image classification will decline. Using the maximum pool method, reducing the step size can improve the classification effect. Using relu function as the activation function can improve the classification accuracy. In the process of large data set processing, appropriately adding a network layer can improve classification accuracy. In the diagnosis and analysis of brain CT images, the overall classification accuracy is close to 70%, and in the diagnosis of tumor diseases, the accuracy is higher, up to 80%.
Identifiants
pubmed: 35345522
doi: 10.1155/2022/5373624
pmc: PMC8957435
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5373624Informations de copyright
Copyright © 2022 Qiufang Ma.
Déclaration de conflit d'intérêts
The author declares that they have no conflicts of interest.
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