ICTACT Journal on Image and Video Processing

G.M. Karpura Dheepan ¹, Shaik Mohammed Rafee ², Prasanthi Badugu ³, Sunil Kumar ⁴

Affiliations
1 Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, IN
2 Department of Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering, IN
3 Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University, Kakinada, IN
4 Department of Commerce and Management, SGT University, IN

Abstract

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.

Keywords

Transfer Learning, Convolutional Neural Networks, Medical Image Compression, Multimedia, Data Efficiency.

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