Medical image compression plays a pivotal role in efficient data storage and transmission, crucial for modern healthcare systems. This research proposes a convolutional transfer learning technique scheme tailored for multimedia data compression, specifically targeting medical images. In the background, the growing volume of medical imaging data and the demand for efficient storage and transmission underscore the need for innovative compression methods. Leveraging transfer learning from pre-trained convolutional neural networks (CNNs) designed for image recognition tasks, our methodology optimizes the compression process for medical images. The proposed scheme utilizes a pre-trained CNN’s feature extraction capabilities to capture relevant patterns in medical images, followed by fine-tuning on a specialized dataset. This approach capitalizes on the inherent ability of CNNs to learn hierarchical representations, enhancing the compression model’s adaptability to medical imaging nuances. The contribution of this research lies in the development of a tailored transfer learning scheme that effectively balances generic feature extraction and domain-specific adaptation for medical images. Results demonstrate significant improvements in compression efficiency, preserving diagnostic information while achieving substantial data reduction. The proposed scheme showcases promise for enhancing medical image storage, transmission, and retrieval systems, contributing to the advancement of healthcare technology.
G.M. Karpura Dheepan1, Shaik Mohammed Rafee2, Prasanthi Badugu3, Sunil Kumar4
Sathyabama Institute of Science and Technology, India1, Sasi Institute of Technology and Engineering, India2, Jawaharlal Nehru Technological University, Kakinada, India3, SGT University, India4
Transfer Learning, Convolutional Neural Networks, Medical Image Compression, Multimedia, Data Efficiency
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 14 , Issue: 3 , Pages: 3169 - 3174 )
Date of Publication :
February 2024
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135
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