Hybrid Deep Learning-Based Compression for Preserving Medical Diagnostic Integrity
DOI:
https://doi.org/10.63949/crinfo.v1i1.003Keywords:
- Medical Image,
- Image compression,
- Deep Learning,
- CNN
Abstract
Medical image compression is essential for maintaining diagnostic accuracy while reducing the storage and transmission requirements of medical image data. This study presents a hybrid deep learning model that integrates conventional transform coding methods, including wavelet and discrete cosine transform for frequency domain coding, autoencoders for dimensionality reduction, and convolutional neural networks or spatial feature extraction. The approach significantly reduces the dimensions of medical images and achieves high compression ratios while preserving essential diagnostic information. Performance analysis shows that the proposed model outperforms previous methods, achieving a compression rate of 68%, a peak signal-to-noise ratio (PSNR) of 46, and a structural similarity index (SSIM) of 0.91. The results indicate that the model achieves exceptional compression efficiency and image quality, making it suitable for practical medical imaging applications that require significant compression and image quality preservation.
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References
[1] Pankaj S, Dua M. Chaos based Medical Image Encryption Techniques: A Comprehensive Review and Analysis. Information Security Journal: A Global Perspective. 2024 May 3;33(3):332-58.
[2] Liu X, Wang M, Wang S, Kwong S. Bilateral Context Modeling for Residual Coding in Lossless 3D Medical Image Compression. IEEE Transactions on Image Processing. 2024 Mar 25.
[3] Vikraman BP, Jabeena A. Segmentation based medical image compression of brain magnetic resonance images using optimized convolutional neural network. Multimedia Tools and Applications. 2024 Mar;83(9):26643-61.
[4] Dodda S, Chintala S, Kanungo S, Adedoja T, Sharma S. Exploring AI-driven Innovations in Image Communication Systems for Enhanced Medical Imaging Applications. Journal of Electrical Systems. 2024;20(3s):949-59.
[5] Liao H, Li Y. LC-TMNet: learned lossless medical image compression with tunable multi-scale network. PeerJ Computer Science. 2024 Dec 20;10:e2511.
[6] Singh S, Tripathi S, Jain T, Rana S, Choudhary N. An Efficient and Privacy-Preserving Medical Image Compression and Sharing Protocol. In2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) 2024 Jun 24 (pp. 1-7). IEEE.
[7] Lone MR. A high speed and memory efficient algorithm for perceptually-lossless volumetric medical image compression. Journal of King Saud University-Computer and Information Sciences. 2022 Jun 1;34(6):2964-74.
[8] Tensmeyer C, Martinez T. Historical document image binarization: A review. SN Computer Science. 2020 May;1(3):173.
[9] Zhang Y, Deng L, Zhu H, Wang W, Ren Z, Zhou Q, Lu S, Sun S, Zhu Z, Gorriz JM, Wang S. Deep learning in food category recognition. Information Fusion. 2023 Oct 1;98:101859.
[10] Ahuja B, Doriya R. An Unsupervised Learning Approach for Visual Data Compression with Chaotic Encryption. In2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) 2021 Sep 15 (pp. 1-4). IEEE.
[11] Lim ST, Abd Manap NB. A region-based compression technique for medical image compression using principal component analysis (pca). International Journal of Advanced Computer Science and Applications. 2022;13(2).
[12] Fan Z, Rong X, Yu X. Region of interest based medical image compression using block-to-row bidirectional principal component analysis. ICIC Express Letters. 2017;11(12):1765-72.
[13] Rao GS, Rani SS, Rao BP. Performance analysis of CT scan and X-Ray medical images compression using PCA and WDR techniques. InProceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur-India 2019 Feb 26.
[14] Fettah A, Menassel R, Gattal A, Gattal A. Convolutional Autoencoder-Based medical image compression using a novel annotated medical X-ray imaging dataset. Biomedical Signal Processing and Control. 2024 Aug 1;94:106238.
[15] Monika R, Dhanalakshmi S. An efficient medical image compression technique for telemedicine systems. Biomedical Signal Processing and Control. 2023 Feb 1;80:104404.
