Optimal Medical Image Size Reduction Model Creation Using Recurrent Neural Network and GenPSOWVQ Article Swipe
Chethana Sridhar
,
Piyush Kumar Pareek
,
R. Kalidoss
,
Sajjad Shaukat Jamal
,
Prashant Kumar Shukla
,
Stephen Jeswinde Nuagah
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/2354866
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/2354866
Medical diagnosis is always a time and a sensitive approach to proper medical treatment. Automation systems have been developed to improve these issues. In the process of automation, images are processed and sent to the remote brain for processing and decision making. It is noted that the image is written for compaction to reduce processing and computational costs. Images require large storage and transmission resources to perform their operations. A good strategy for pictures compression can help minimize these requirements. The question of compressing data on accuracy is always a challenge. Therefore, to optimize imaging, it is necessary to reduce inconsistencies in medical imaging. So this document introduces a new image compression scheme called the GenPSOWVQ method that uses a recurrent neural network with wavelet VQ. The codebook is built using a combination of fragments and genetic algorithms. The newly developed image compression model attains precise compression while maintaining image accuracy with lower computational costs when encoding clinical images. The proposed method was tested using real-time medical imaging using PSNR, MSE, SSIM, NMSE, SNR, and CR indicators. Experimental results show that the proposed GenPSOWVQ method yields higher PSNR SSIMM values for a given compression ratio than the existing methods. In addition, the proposed GenPSOWVQ method yields lower values of MSE, RMSE, and SNR for a given compression ratio than the existing methods.
Related Topics
Concepts
Computer science
Image compression
Codebook
Artificial intelligence
Automation
Data compression
Reduction (mathematics)
Artificial neural network
Encoding (memory)
Medical imaging
Peak signal-to-noise ratio
Compression ratio
Data mining
Image processing
Computer vision
Image (mathematics)
Mathematics
Engineering
Internal combustion engine
Mechanical engineering
Automotive engineering
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/2354866
- https://downloads.hindawi.com/journals/jhe/2022/2354866.pdf
- OA Status
- hybrid
- Cited By
- 77
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4214605467
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4214605467Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2022/2354866Digital Object Identifier
- Title
-
Optimal Medical Image Size Reduction Model Creation Using Recurrent Neural Network and GenPSOWVQWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-02-26Full publication date if available
- Authors
-
Chethana Sridhar, Piyush Kumar Pareek, R. Kalidoss, Sajjad Shaukat Jamal, Prashant Kumar Shukla, Stephen Jeswinde NuagahList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/2354866Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/jhe/2022/2354866.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/jhe/2022/2354866.pdfDirect OA link when available
- Concepts
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Computer science, Image compression, Codebook, Artificial intelligence, Automation, Data compression, Reduction (mathematics), Artificial neural network, Encoding (memory), Medical imaging, Peak signal-to-noise ratio, Compression ratio, Data mining, Image processing, Computer vision, Image (mathematics), Mathematics, Engineering, Internal combustion engine, Mechanical engineering, Automotive engineering, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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77Total citation count in OpenAlex
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2025: 6, 2024: 13, 2023: 23, 2022: 35Per-year citation counts (last 5 years)
- References (count)
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35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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