Authors :
Tanushree Bharti; Yogam Singh; Mudit Jain; Ankita Kumari
Volume/Issue :
Volume 9 - 2024, Issue 12 - December
Google Scholar :
https://tinyurl.com/ms68734e
Scribd :
https://tinyurl.com/3wnsa3vt
DOI :
https://doi.org/10.5281/zenodo.14557003
Abstract :
Improving the quality of medical images is
essential for precise diagnosis and treatment planning.
When low quality images are used to train the neural
network model, the good accuracy cannot be achieved.
Nowadays, Generative Adversarial Networks (GANs)
have become a potent image enhancement tool that can
provide a fresh method for raising the caliber of medical
images. In order to improve medical images, this paper
presents a GAN-based framework that reduces noise,
increases resolution, and corrects artifacts. The suggested
technique makes use of a generator network to convert
low-quality images into their high-quality equivalents,
and a discriminator network to assess the veracity of the
improved images. To ensure robustness across various
modalities, the model is trained on a diverse dataset of
medical images, including MRI, CT, and X-ray scans.
Our experimental results show that GAN-based method
significantly improves the image quality when compared
to conventional methods, as evidenced by enhanced peak
signal-to-noise ratio (PSNR) and structural similarity
index (SSIM) according to quantitative evaluations. This
study emphasizes the value of incorporating deep
learning methods into medical image processing pipelines
and the potential of GANs to advance medical imaging
technology so that a robust neural network model can be
designed.
Keywords :
Medical Images Quality, Convolutional Neural Networks, Generative Adversarial Networks (GANs), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM).
References :
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- Eunji Choi et al. "Stargan: Unified generative adversarial networks for multi-domain image-to-image translation." In 2018's IEEE Conference on Computer Vision and Pattern Recognition Proceedings, pp. 8789–8797.
- Jelmer M. Wolterink and colleagues "Generative adversarial networks for noise reduction in low-dose CT." IEEE Medical Imaging Transactions 36.12, 2017; 2536–2545.
- Maayan Frid-Adar et al. "Synthetic data augmentation using GAN for improved liver lesion classification." In: International Workshop on Medical Imaging Simulation and Synthesis, pages 1-11. Cham, Springer, 2018.
- Armanious, Kristina, et al. "Using adversarial networks to synthesize CT from ultrasound images." In International Conference on Computer-Assisted Intervention and Medical Image Computing, pp. 81–89. Springer, Cham (2017).
- Qiaoying Yang et al. "Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss." 2018 IEEE Access 6: 47958–47966.
- Chunyuan Li and colleagues, "Unsupervised image-to- image translation networks." In Neural Information Processing Systems Advances, pp. 700–708 in 2017.
- Yibin Song and colleagues, "Liver lesion detection and classification with novel neural network architectures." In: International Conference on Computer-Assisted Intervention and Medical Image Computing, 830-838. Springer, Cham (2017).
- Zhu, Jun-Yan, and associates. "Toward multimodal image-to-image translation." Pages 465–476 in Advances in Neural Information Processing Systems, 2017.
- Zhang, et al., "Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network." In International Conference on Computer-Assisted Intervention and Medical Image Computing, pages 56–64. Cham, Springer, 2018.
- Kavita Lal, Madan Lal Saini; A study on deep fake identification techniques using deep learning. AIP Conf. Proc. 15 June 2023; 2782 (1): 020155. https://doi.org/10.1063/5.0154828
- Y. Singh, M. Saini and Savita, "Impact and Performance Analysis of Various Activation Functions for Classification Problems," 2023 IEEE International Conference on Contemporary Computing and Communications (InC4), Bangalore, India, 2023, pp. 1-7, doi: 10.1109/InC457730.2023.10263129.
- Sarmah, J., Saini, M.L., Kumar, A., Chasta, V. (2024). Performance Analysis of Deep CNN, YOLO, and LeNet for Handwritten Digit Classification. In: Sharma, H., Chakravorty, A., Hussain, S., Kumari, R. (eds) Artificial Intelligence: Theory and Applications. AITA 2023. Lecture Notes in Networks and Systems, vol 844. Springer, Singapore. https://doi.org/10.1007/978-981-99-8479-4_16
- M. L. Saini, A. Patnaik, Mahadev, D. C. Sati and R. Kumar, "Deepfake Detection System Using Deep Neural Networks," 2024 2nd International Conference on Computer, Communication and Control (IC4), Indore, India, 2024, pp. 1-5, doi: 10.1109/IC457434.2024.10486659.
- P. D. S. Prasad, R. Tiwari, M. L. Saini and Savita, "Digital Image Enhancement using Conventional Neural Network," 2023 2nd International Conference for Innovation in Technology (INOCON), Bangalore, India, 2023, pp. 1-5, doi: 10.1109/INOCON57975.2023.10100995.
- Skandarani, Youssef, Pierre-Marc Jodoin, and Alain Lalande. "Gans for medical image synthesis: An empirical study." Journal of Imaging 9.3 (2023): 69.
- Showrov, Atif Ahmed, et al. "Generative Adversarial Networks (GANs) in Medical Imaging: Advancements, Applications and Challenges." IEEE Access (2024).
- E. G. Kumar, M. Lal Saini, S. A. Khadar Ali and B. B. Teja, "A Clinical Support System for Prediction of Heart Disease using Ensemble Learning Techniques," 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA), Theni, India, 2023, pp. 926-931, doi: 10.1109/ICSCNA58489.2023.10370569
Improving the quality of medical images is
essential for precise diagnosis and treatment planning.
When low quality images are used to train the neural
network model, the good accuracy cannot be achieved.
Nowadays, Generative Adversarial Networks (GANs)
have become a potent image enhancement tool that can
provide a fresh method for raising the caliber of medical
images. In order to improve medical images, this paper
presents a GAN-based framework that reduces noise,
increases resolution, and corrects artifacts. The suggested
technique makes use of a generator network to convert
low-quality images into their high-quality equivalents,
and a discriminator network to assess the veracity of the
improved images. To ensure robustness across various
modalities, the model is trained on a diverse dataset of
medical images, including MRI, CT, and X-ray scans.
Our experimental results show that GAN-based method
significantly improves the image quality when compared
to conventional methods, as evidenced by enhanced peak
signal-to-noise ratio (PSNR) and structural similarity
index (SSIM) according to quantitative evaluations. This
study emphasizes the value of incorporating deep
learning methods into medical image processing pipelines
and the potential of GANs to advance medical imaging
technology so that a robust neural network model can be
designed.
Keywords :
Medical Images Quality, Convolutional Neural Networks, Generative Adversarial Networks (GANs), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM).