Abstract
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.
Highlights
Modern learning reports that the employment of imaging has increased significantly in the previous two decades [1]
Imaging technology is implemented by X-ray, ultrasound, MRI/fMRI, nuclear medicine, PET, CT [3], and DXA. e necessity of saving, distributing, and downloading these images in their own appearance has given to the picture archiving along with communication system Journal of Healthcare Engineering (PACS) a medical imaging skill that presents low-cost storage as well as access to useful images from many sources
Das et al [17] described a lossless medical imaging watermark (MIW) procedure that relies on the area of interest concept. e major purpose of this technique is to offer clarification to many issues related to the dissemination of medical data
Summary
Modern learning reports that the employment of imaging has increased significantly in the previous two decades [1]. In the United States, for example, there was a study by SmithBrinman et al [2] It was reported in 2008 that CCTV images doubled and MRI images doubled. The transition from analog to digital provides fast, stress-free, and accurate image development. All of these factors have supplied significantly to the group of images. The newly developed method is to reduce the image size sufficiently as well as speed up the hardware execution of the software in real time. E final outputs show that the newly developed method offers a compression ratio of 400% compared to other traditional approaches. Das et al [17] described a lossless medical imaging watermark (MIW) procedure that relies on the area of interest concept. e major purpose of this technique is to offer clarification to many issues related to the dissemination of medical data
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