Medical Image Analysis of Lung Tumor Diagnosis Based on Generalized Fuzzy C-Means Clustering Algorithm

In recent years, the rapid development of medical imaging technology has brought medical image analysis into the era of big data. CT imaging technology is one of the most common imaging methods for disease screening. This paper introduces medical image analysis of lung tumor diagnosis based on Gener...

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Vydáno v:Journal of physics. Conference series Ročník 2467; číslo 1; s. 12004 - 12012
Hlavní autoři: zwaid, Jassim Mohammed Ahmed, Murad, Masrah Azrifah Azmi, Khalid, Fatimah binti, Manshor, Noridayu, Al-Jumaily, Abdulmajeed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.05.2023
Témata:
Algorithms
Big Data
Clustering
Computed tomography
Computer vision
Deep learning
Diagnosis
Fuzzy c-means clustering algorithm
Image analysis
Lung tumor
Medical image
Medical imaging
Physics
Tumors
ISSN:1742-6588, 1742-6596
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Shrnutí:In recent years, the rapid development of medical imaging technology has brought medical image analysis into the era of big data. CT imaging technology is one of the most common imaging methods for disease screening. This paper introduces medical image analysis of lung tumor diagnosis based on Generalized fuzzy c - means clustering algorithm was proposed. Brief introduce lung CT imaging to diagnose lung tumors requires a huge workload, which is often accompanied by long-term image reading and subjective evaluation. The experimental data comes from 15 patient samples which include two groups of samples are selected as test objects. The result shows the positive rate is high, resulting in misdiagnosis. In addition, great success of deep learning technology in the field of computer vision has made it possible to realize computer-aided diagnosis and screening of lung cancer.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2467/1/012004