ArticleJournal of healthcare engineering2022
IntOPMICM: Intelligent Medical Image Size Reduction Model.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Deep learning based medical image compression using cross attention learning and wavelet transform.Scientific reports · 2025Article
- Improved UNet Deep Learning Model for Automatic Detection of Lung Cancer Nodules.Computational intelligence and neuroscience · 2023Article
- Content-Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm.Computational intelligence and neuroscience · 2023Article
- Detection of Malicious Cloud Bandwidth Consumption in Cloud Computing Using Machine Learning Techniques.Computational intelligence and neuroscience · 2022Article
- Region-Based Segmentation and Classification for Ovarian Cancer Detection Using Convolution Neural Network.Contrast media & molecular imaging · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the increasing number of medical imaging images being utilized for the diagnosis and treatment of diseases, lossy or improper image compression has become more prevalent in recent years. The compression ratio and image quality, which are commonly quantified by PSNR values, are used to evaluate the performance of the lossy compression algorithm. This article introduces the IntOPMICM technique, a new image compression scheme that combines GenPSO and VQ. A combination of fragments and genetic algorithms was used to create the codebook. PSNR, MSE, SSIM, NMSE, SNR, and CR indicators were used to test the suggested technique using real-time medical imaging. The suggested IntOPMICM approach produces higher PSNR SSIM values for a given compression ratio than existing methods, according to experimental data. Furthermore, for a given compression ratio, the suggested IntOPMICM approach produces lower MSE, RMSE, and SNR values than existing methods.
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Registered trials
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