Deep Learning Autoencoder for denoising Medical Images

Akeem Abimbola Raji; Martins Osifeko; Mubarak Abdulkareem; Enoch Oduneye

doi:10.35377/saucis...1735782

EN

Deep Learning Autoencoder for denoising Medical Images

Abstract

Medical image examination is crucial for accurate diagnosis and treatment plan of medical disorders in the human body. Medical images are corrupted by noise, which obscures important details, leading to misinterpretation and wrong diagnosis of the images. Image denoising methods are employed to eliminate noise with a view to improving image quality and preserving its essential features. This work utilizes Deep Learning Autoencoder (DLA) for removing noise in medical images, and its performance is compared with U-Net (segmentation model), which, to the best of our knowledge, has not been presented in this manner in the literature. The metrics utilized for comparison are image quality, Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index Measure (SSIM). It is found that DLA outperforms U-Net, producing better image quality and, in addition, has lower MSE, higher PSNR and SSIM. For instance, the MSE, PSNR, and SSIM of DLA when used to denoise noisy lung cancer images are 0.0226, 16.584dB, and 0.3845, respectively, while those of U-Net are 0.2487, 6.118dB, and 0.04760. In addition, it is found that the performance of DLA surpasses that reported for the state-of-the-art models in the literature.

Keywords

Ethical Statement

All ethical standards are followed in developing the manuscript.

References

A.A. Raji, O.I. Adebisi, O.A. Akinola, and A.B. Ogundare, “A bimodal technique for enhancement of picture quality of medical images”, Journal of Engineering and Technology for Industrial Applications, vol. 11, no 53, pp. 177-185, 2025
B. Goyal, A. Gogra, S. Agrawal, and B.S. Sohi, “Noise issues prevailing in various types of medical images,” Biomedical & Pharmacology Journal, vol. 11, no. 3, pp. 1227-1237, 2018.
R. Rajni, and A. Anutam, “Image denoising techniques- An overview”, International Journal of Computer Applications, vol. 86, no. 16, pp. 13-17, 2014. doi: 10.5120/15069-3436
M.C. Motwani, M.C. Gadiya, R.C. Motwani, and F.C. Harris, “Survey of image denoising techniques”, in Proc. Global Signal Processing Expo and Conf. (GSPx), Santa Clara, CA, USA, Sep. 2004, pp. 27-30.
P. Bhardwaj, P.K. Gupta, H. Panwar, M.K. Siddiqui, R. Morales-Menendez, and A. Bhaik, “Applications of deep learning on student engagement in e-learning environment,” Computer and Electrical Engineering, vol. 93, 2021, pp. 107277. doi: 10.1016/j.compeleceng.2021.107277
P. Hastings, S. Hughes, and M.A. Britt, “Active Learning for Improving Machine Learning of Student Explanatory Essays,” Artificial Intelligence in Education, pp. 140-153, 2018. doi: 10.1007/978-3-319-93843-1_11
M.K. Siddiqui, R. Morales-Menendez, P.K. Gupta, H. Iqbai, F. Hussain, K. Khatoon, and S. Ahmad, “Correlation between temperature and COVID-19 (suspected, confirmed and death) cases based on machine learning analysis”, Journal of Pure Applied Microbiology, vol. 14, 2020.
A. S. Albahri et al., “A systematic review of trustworthy artificial intelligence applications in natural disasters”, Computer and Electrical Engineering, vol. 118, pp. 1-53, 2024. doi: 10.1016/j.compeleceng.2024.109409

H. Wang, G. Barone, and A. Smith, “Current and future role of data fusion and machine learning in infrastructural health monitoring”, Structural Infrastructure Engineering, vol. 20, no. 12, pp. 1853-1882, 2023. doi: 10.1080/15732479.2023.2165118
A. De vries, N. Blinznyuk, and P. Pinedo, “Invited review: examples and opportunities for artificial intelligence (AI) in dairy farms”, Applied Animal Science, vol. 3, no. 1, pp. 14-22, 2023.
N. Diffellah, T. Bekkouche, and R. Hamadini, “Image denoising algorithms using norm minimization technique”, in Proceedings of 2nd International Conference on Complex Systems and their Applications, 2021.
X. Yang, Y. Xu, Y. Quan, and H. Ji, “Image denoising via sequential ensemble learning”, IEEE Transactions of Image Processing, vol. 29, pp. 5038 - 5049, 2020. doi: 10.1109/TIP.2020.2978645
V. Karnarti, M. Uliyar, and S.Dey, “Fast non-local algorithm for image denoising”, In 2009 16th IEEE International Conference on Image Processing (CICIP), pp. 3873-3876, 2009. doi: 10.1109/ICIP.2009.5414044
A. Buades, B. Coll, and J-M Morel, “A non- local algorithm for image denoising”, In Proceedings of 2005 IEEE Computer Vision and Pattern Recognition (CVPR’05), 2005. doi: 10.1109/CVPR.2005.38
L.M. Satapathy, and P. Das, “A new approach of image denoising based on adaptive multi-resolution technique”, Nigerian Journal of Technological Development, vol. 19, no. 1, pp. 92-100, 2022. doi: 10.4314/njtd.v19i1.10
M. Lebrun, M. Colom, and J.M Morel, “The noise clinic: a blind image denoising algorithm”, Image Processing Online, vol. 5, pp. 1-54, 2015. doi: 10.5201/ipol.2015.125
J. Gurrola-Ramos, O. Dalman, and T.E. Alarcon, “A residual dense U-Net neural network for image denoising”, IEEE Access, vol. 9, pp. 31742 - 31754, 2021. doi: 10.1109/ACCESS.2021.3061062
J. F. J. Abascal, S. Bussod, N. Ducros, S. Si-Mohammed, P. Douek, C. Chappard, and F. A. Peyrin, “A residual U-Net network with image prior for 3D image denoising,” in Proc. 28th Eur. Signal Process. Conf. (EUSIPCO), 2021, doi: 10.23919/EUSIPCO47968.2020.9287607.
R. Komatsu, and T. Gonsalves, “Comparing U-Net based models for denoising color images”, AI, vol. 1, no. 4, pp.465-486, 2020. doi: 10.3390/ai1040029
J. Han, J. Choi, and C. Lee, “Image denoising method based on deep learning using improved U-Net”, IEEE Transactions on Smart Processing and Computing, vol. 10, no. 4, pp. 291-295, 2021. doi: 10.5573/IEIESPC.2021.10.4.291
F. Jia, W.H. Wong, and T. Zang, “DDUNet: Dense Dense U-Net with application in image denoising”, in Proceedings of 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 354-364, 2021. doi: 10.1109/ICCVW54120.2021.00044
Z. Lian, and H.Wang, “An image debluring method using improved U-Net model based on multilayer fusion and attention mechanism”, Scientific Reports, vol. 13, no. 21402, pp. 1-13, 2023. doi: 10.1038/s41598-023-47768-4
K. Nagano, Y. Mukouyama, T. Nishimura, H. Fujioka, K. Watanbe, T. Kurita, and A. Hidaka, “Noise reduction of SEM images using U-Net with SSIM loss function”, in Proc. 52nd Int. Symp. Stochastic Syst. Theory and Its Applicat. (ISCIE), 2020, pp. 65–72.
A. Rajwadel, A. Rangarajan, and A. Banerjee, “Image denoising using the higher order singular value decomposition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no.4, pp. 849-862, 2013. doi: 10.1109/TPAMI.2012.140
Q. Xiang, Y. Tang, and X. Zhou, “Multi-task learning with self-learning weight for image denoising”, Journal of Engineering and Applied Science, vol. 71, no. 93, pp. 1-15, 2024. doi: 10.1186/s44147-024-00425-7
M.B. Davici, and Z.A. Erdem, “Comparative study on denoising from facial images using convolutional Autoencoder”, GUJ Science, vol. 36, no. 3, pp. 1122-1138, 2023.
A. Kumar, and S.S Sodhi, “Comparative analysis of Gaussian filter, median filter and denoise autoencoder”, in Proceeding of 2020 7th International Conference on Computing for Global Development, pp. 45-47, 2020. doi: 10.23919/INDIACom49435.2020.9083712
N.M. Tun, A.I. Gavrilov, and N.L. Tun, “Facial image denoising using convolutional Autoencoder Network”, in Proceedings of 2020 International Conference on Industrial Engineering Applications and Manufacturing (ICIEAM), 2020. doi: 10.1109/ICIEAM48468.2020.9112080
M. Nishio et al., “Convolutional Autoencoder for image denoising of ultra-low dose CT”, Heliyon, vol. 3, no. e003903, 2017. doi:10.1016/j.heliyon.2017.e00393
L. Gondara, “Medical image denoising using convolutional denoising Autoencoder”, in Proc. 16th IEEE Int. Conf. Data Mining Workshops (ICDMW), pp. 241-246, 2016. doi: 10.1109/ICDMW.2016.0041
K. Bajaj, D.K. Singh, and M.A. Ansari, “Autoencoders based deep learner for image denoising”, Procedia Computer Science, vol. 171, pp. 1535-1541, 2021.
Y. Farooq, and S. Savas, “Noise removal from the image using convolutional neural network-based denoising autoencoder”, Journal of emerging computer technologies, vol. 3, no. 1, pp. 21-28, 2023. doi: 10.57020/ject.1390428
E. Centinkaya, and M.F. Kirac, “Image denoising using deep convolutional autoencoder with feature pyramids”, Turkish Journal of Electrical Engineering & Computer Sciences, vol. 28, pp. 2096-2109, 2020.
Medical image datasets, “Kaggle online repository”, [Online]. Available: https://www.kaggle.com . [Accessed: Mar. 17, 2025].
O. Ronnerberger, P. Fischer, and T. Box, “U-Net: Convolutional network for biomedical image segmentation”, in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, New York, USA, pp. 234-241.
D. Murthy, M.S. Reddy, G. Krishnachaitanya, K. Bilvasai, and V. Durgeshi, “Image denoising techniques on medical and microscopic images”, International Journal of Novel Research and Development (IJNRD), vol. 8, no 3, pp. 84-89, 2023.

Details

Primary Language

English

Subjects

Empirical Software Engineering , Computer Software

Journal Section

Research Article

Authors

Akeem Abimbola Raji ^*
0000-0003-4303-4940
Nigeria

Martins Osifeko
0000-0002-9350-6056
Nigeria

Mubarak Abdulkareem
0009-0002-5832-2267
Nigeria

Enoch Oduneye
0009-0001-0056-088X
Nigeria

Early Pub Date

March 15, 2026

Publication Date

March 15, 2026

Submission Date

July 6, 2025

Acceptance Date

November 6, 2025

Published in Issue

Year 2026 Volume: 9 Number: 1

DOI

https://doi.org/10.35377/saucis...1735782

IZ

https://izlik.org/JA47RW37DG

Cite

RIS / Bibtex

APA

Raji, A. A., Osifeko, M., Abdulkareem, M., & Oduneye, E. (2026). Deep Learning Autoencoder for denoising Medical Images. Sakarya University Journal of Computer and Information Sciences, 9(1), 76-89. https://doi.org/10.35377/saucis...1735782

AMA

1.Raji AA, Osifeko M, Abdulkareem M, Oduneye E. Deep Learning Autoencoder for denoising Medical Images. SAUCIS. 2026;9(1):76-89. doi:10.35377/saucis.1735782

Chicago

Raji, Akeem Abimbola, Martins Osifeko, Mubarak Abdulkareem, and Enoch Oduneye. 2026. “Deep Learning Autoencoder for Denoising Medical Images”. Sakarya University Journal of Computer and Information Sciences 9 (1): 76-89. https://doi.org/10.35377/saucis. 1735782.

EndNote

Raji AA, Osifeko M, Abdulkareem M, Oduneye E (March 1, 2026) Deep Learning Autoencoder for denoising Medical Images. Sakarya University Journal of Computer and Information Sciences 9 1 76–89.

IEEE

[1]A. A. Raji, M. Osifeko, M. Abdulkareem, and E. Oduneye, “Deep Learning Autoencoder for denoising Medical Images”, SAUCIS, vol. 9, no. 1, pp. 76–89, Mar. 2026, doi: 10.35377/saucis...1735782.

ISNAD

Raji, Akeem Abimbola - Osifeko, Martins - Abdulkareem, Mubarak - Oduneye, Enoch. “Deep Learning Autoencoder for Denoising Medical Images”. Sakarya University Journal of Computer and Information Sciences 9/1 (March 1, 2026): 76-89. https://doi.org/10.35377/saucis. 1735782.

JAMA

1.Raji AA, Osifeko M, Abdulkareem M, Oduneye E. Deep Learning Autoencoder for denoising Medical Images. SAUCIS. 2026;9:76–89.

MLA

Raji, Akeem Abimbola, et al. “Deep Learning Autoencoder for Denoising Medical Images”. Sakarya University Journal of Computer and Information Sciences, vol. 9, no. 1, Mar. 2026, pp. 76-89, doi:10.35377/saucis. 1735782.

Vancouver

1.Akeem Abimbola Raji, Martins Osifeko, Mubarak Abdulkareem, Enoch Oduneye. Deep Learning Autoencoder for denoising Medical Images. SAUCIS. 2026 Mar. 1;9(1):76-89. doi:10.35377/saucis. 1735782

Öz

Deep Learning Autoencoder for denoising Medical Images

Abstract

Keywords

Ethical Statement

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite