Utilizing Deep Learning Techniques to Diagnose Nodules in Lung Computed Tomography (CT) Scan Images

被引：0

作者：

Saxena, Sugandha ^{[1
]}

Prasad, S.N. ^{[2
]}

Murthy, T. S Deepthi ^{[3
]}

机构：

[1] School of Electronics and Communication Engineering, REVA University, Karnataka, Bangalore,560064, India

[2] Department of Electrical and Electronics Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Karnataka, Manipal,576104, India

[3] School of Electronics and Communication Engineering, REVA University, Karnataka, Bangalore,560064, India

来源：

IAENG International Journal of Computer Science | 2023年 / 50卷 / 02期

关键词：

Biological organs - Computerized tomography - Deep learning - Diseases - Image segmentation - K-means clustering - Learning systems - Magnetic resonance imaging - Patient treatment;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

There are different methods available for detecting lung cancer including CT (Computed Tomography) scan, MRI(Magnetic Resonance Imaging) scan etc. Among all methods, CT scan images are preferred more because they can detect a very small nodule in the lungs. Early treatment can be given to patients if it is diagnosed at early stages, hence reducing the number of deaths. This paper shows that Median Filter outperformed the Average, Gaussian, Laplacian, and Wiener Filters in the preprocessing stage for the removal of noise from images. Additionally, a study has been conducted on several image segmentation algorithms, such as clustering, watershed, and Thresholding segmentation. This was followed by the extraction and classification of nodules. Different performance parameters have been calculated to validate the results of the model and it is discovered that proposed model has greatest performance. © 2023,IAENG International Journal of Computer Science. All Rights Reserved.

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