Optimal Denoising System for Medical Images Using Recurrent Neural Network and SVM

Authors

  • Vidhya V, Rajavarman V.N., S. Kevin Andrews, R. Shobarani

Keywords:

Recurrent Neural Networks, Support vector Machine, image denoising, Long Short-Term Memory

Abstract

Image denoising serves as a crucial preprocessing step in the realm of medical image analysis, with the primary objective of faithfully reconstructing the original image from its noisy counterpart. This process is essential for maintaining the integrity of vital details, such as edges and textures, within the denoised image. Innovatively addressing this challenge, our proposed system introduces a novel approach that seamlessly integrates Recurrent Neural Network (RNN) and Support Vector Machine (SVM). This powerful combination is adept at efficiently eliminating various types of noise, including gaussian, white noise, salt and pepper noise, and speckle noise, from intricate lung CT images. To enhance both learning accuracy and training efficiency, we have incorporated batch normalization in conjunction with residual learning. Notably, batch normalization is executed with the support of Long Short-Term Memory (LSTM). This strategic integration aids in the gradual separation of image structure from the noisy observations, a pivotal aspect in achieving optimal denoising outcomes. This approach not only enhances the accuracy of denoising but also contributes to reducing the overall training time, making it a valuable advancement in medical image preprocessing.

Downloads

Download data is not yet available.

References

Manjón, JV, Carbonell-Caballero, J, Lull, JJ, García-Martí, G, MartíBonmatí, L & Robles, M 2008, ‘MRI denoising using non-local means’, Medical image analysis, vol. 12, no. 4, pp. 514-523.

Mitiche, L, Adamou-Mitiche, ABH & Naimi, H 2013, ‘Medical image denoising using dual tree complex thresholding wavelet transform’, IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp. 1-5.

Lone, AH, Siddiqui, AN & Nazir, N 2018, ‘Noise models in digital image processing’, Advance and Innovative Research, vol. 5, no. 4, pp. 66-305.

Kaur J & Kaur R 2014, ‘Digital Image De-Noising Filters A Comprehensive Study’, International Journal of Research In Computer Applications And Robotics, vol. 2, no. 4, pp. 105-111

Verma, R & Ali, J 2013, ‘A comparative study of various types of image noise and efficient noise removal techniques’, International Journal of advanced research in computer science and software engineering, vol. 3, no. 10, pp. 617-622.

Mredhula, L &Dorairangasamy, MA 2013, ‘An extensive review of significant researches on medical image denoising techniques’, International Journal of Computer Applications, vol. 64, no. 14, pp. 1-12.

Manjón, JV, Coupé, P, Martí‐Bonmatí, L, Collins, DL & Robles, M 2010, ‘Adaptive non‐local means denoising of MR images with spatially varying noise levels’, Journal of Magnetic Resonance Imaging, vol. 31, no. 1, pp. 192-203.

Kaur, L, Gupta, S, Chauhan, RC & Saxena, SC 2007, ‘Medical ultrasound image compression using joint optimization of thresholding quantization and best-basis selection of wavelet packets’, Digital Signal Processing, vol. 17, no. 1, pp. 189-198.

Meyer, D & Wien, FT 2015, ‘Support vector machines’, The Interface to lib svm in package, pp. 1-8.

Mehr, AD, Nourani, V, Khosrowshahi, VK & Ghorbani, MA 2019, ‘A hybrid support vector regression–firefly model for monthly rainfall forecasting’, International Journal of Environmental Science and Technology, vol. 16, no. 1, pp. 335-346.

Mohammadi, K, Shamshirband, S, Danesh, AS, Zamani, M &Sudheer, C 2015, ‘Horizontal global solar radiation estimation using hybrid SVM-firefly and SVM-wavelet al.gorithms: a case study’, Natural Hazards, pp. 1-18.

Voigtlaender, P, Doetsch, P& Ney, H 2016, ‘Handwriting recognition with large multidimensional long short-term memory recurrent neural networks’, International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 228-233

Laurent, C, Pereyra, G, Brakel, P, Zhang, Y & Bengio, Y 2016, ‘Batch normalized recurrent neural networks’, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2657-2661.

Pereira, C & Yang, XS 2016, ‘Learning parameters in deep belief networks through firefly algorithm’, Proceedings Artificial Neural Networks in Pattern Recognition: 7th IAPR TC3 Workshop, pp. 1-138.

[15] Wang, H, Wang, W, Sun, H &Rahnamayan, S 2016, ‘Firefly algorithm with random attraction’, International Journal of Bio-Inspired Computation, vol. 8, no. 1, pp. 33-41.

Downloads

Published

26.03.2024

How to Cite

Vidhya V. (2024). Optimal Denoising System for Medical Images Using Recurrent Neural Network and SVM. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2198–2207. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5817

Issue

Section

Research Article