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Survey of denoising, segmentation and classification of magnetic resonance imaging for prostate cancer., , , , , , , и . Multim. Tools Appl., 80 (19): 29199-29249 (2021)A survey of denoising techniques for multi-parametric prostate MRI., и . Multim. Tools Appl., 78 (10): 12689-12722 (2019)Deep learning-based classification network for glaucoma in retinal images., , , , , и . Comput. Electr. Eng., (2022)DC-Gnet for detection of glaucoma in retinal fundus imaging., , , , , и . Mach. Vis. Appl., 31 (5): 34 (2020)A New Approach for Information Security using an Improved Steganography Technique., и . J. Inf. Process. Syst., 9 (3): 405-424 (2013)Optic disc and optic cup segmentation from retinal images using hybrid approach., и . Expert Syst. Appl., (2019)Designing of Robust Image Steganography Technique Based on LSB Insertion and Encryption., и . ARTCom, стр. 302-305. IEEE Computer Society, (2009)A comprehensive review of denoising techniques for abdominal CT images., , и . Multim. Tools Appl., 77 (17): 22735-22770 (2018)Denoising of Computed Tomography Images for Improved Performance of Medical Devices in Biomedical Engineering., , и . ICACDS (2), том 1441 из Communications in Computer and Information Science, стр. 136-146. Springer, (2021)Denoising of magnetic resonance imaging using Bayes shrinkage based fused wavelet transform and autoencoder based deep learning approach., , , , , и . Biomed. Signal Process. Control., (2021)