Research on Medical Image Enhancement Method Based on Conditional Entropy Generative Adversarial Networks

[1] Yang, R., Yu, J., Yin, J., Liu, K., & Xu, S. (2022). A dense r-cnn multi-target instance segmentation model and its application in medical image processing. IET image processing(9), 16. Search in Google Scholar

[2] Alnazer, I., Bourdon, P., Urruty, T., Falou, O., & Fernandez-Maloigne, C. (2021). Recent advances in medical image processing for the evaluation of chronic kidney disease. Medical Image Analysis, 101960. Search in Google Scholar

[3] Duan, Y., Li, Y., Lu, L., & Ding, Y. (2022). A faster outsourced medical image retrieval scheme with privacy preservation. Journal of systems architecture(122-), 122. Search in Google Scholar

[4] Jiang, Z., Wang, L., Wu, Q., Shao, Y., Shen, M., & Jiang, W., et al. (2022). Computer-aided diagnosis of retinopathy based on vision transformer. Journal of Innovative Optical Health Sciences. Search in Google Scholar

[5] Yadav, J. K. P. S., & Yadav, S. (2022). Computer-aided diagnosis of cataract severity using retinal fundus images and deep learning. Computational intelligence. Search in Google Scholar

[6] Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., & Nandi, A. K. (2022). Medical image segmentation using deep learning: a survey. IET image processing. Search in Google Scholar

[7] Zhu, Y., Wang, X., Chen, L., & Nie, R. (2022). Cefusion: multimodal medical image fusion via cross encoder. IET image processing. Search in Google Scholar

[8] Ahmet, G. (2021). Computer-aided diagnosis system for chronic obstructive pulmonary disease using empirical wavelet transform on auscultation sounds. The Computer Journal. Search in Google Scholar

[9] A, J. L., A, H. K. L., A, G. J., B, S. G., C, J. B., & A, D. P., et al. (2021). A case study on computer-aided diagnosis of nonerosive reflux disease using deep learning techniques. Neurocomputing, 445, 149-166. Search in Google Scholar

[10] Jaspreet, K., & Prabhpreet, K. (2022). Automated computer-aided diagnosis of diabetic retinopathy based on segmentation and classification using k-nearest neighbor algorithm in retinal images. The Computer Journal(8), 8. Search in Google Scholar

[11] Hu, Q., Wang, S., Chen, C., Kang, S., Sun, Z., & Wang, Y., et al. (2022). Comparison of two reader modes of computer-aided diagnosis in lung nodules on low-dose chest ct scan. Journal of Innovative Optical Health Sciences, 15(2), 2250013. Search in Google Scholar

[12] Nair, R. R., Babu, T., & Singh, T. (2023). Multiresolution approach on medical image fusion by modified local energy. Signal, Image and Video Processing. Search in Google Scholar

[13] Zeng, X., Huang, Z., Xu, L., & Xie, Y. (2022). Cp-gan: meet the high requirements of diagnose report to medical Image by content preservation. IET image processing(1), 16. Search in Google Scholar

[14] Bdair, T., Wiestler, B., Navab, N., & Albarqouni, S. (2022). Roam: random layer mixup for semi-supervised learning in medical images. IET image processing(10), 16. Search in Google Scholar

[15] Zhang, X., & Yan, H. (2021). Medical image fusion and noise suppression with fractional-order total variation and multi-scale decomposition. IET image processing(8), 15. Search in Google Scholar

[16] Li, D., Chen, Y. W., Li, J., Cao, L., Bhatti, U. A., & Zhang, P. (2022). Robust watermarking algorithm for medical images based on accelerated-kaze discrete cosine transform. IET Biometrics. Search in Google Scholar

[17] Zhu, J., Tan, C., Yang, J., Yang, G., & Lio’, P. (2021). Arbitrary scale super-resolution for medical images. International Journal of Neural Systems, 2150037. Search in Google Scholar

[18] Li, S., Huang, G., Xu, X., & Lu, H. (2022). Query-based black-box attack against medical image segmentation model. Future Generation Computer Systems, 133, 331-337. Search in Google Scholar

[19] Sironmani, P. P., & Augasta, M. G. (2023). A novel cnn architecture with an efficient channelization for histopathological medical image classification. Multimedia Tools and Applications. Search in Google Scholar

[20] Parvathy, V. S., Pothiraj, S., & Sampson, J. (2021). Multimodality medical image fusion based on nonsub-sampled contourlet transform. International Journal of Computer Applications in Technology(4), 65. Search in Google Scholar

[21] Zhu, F., Zhu, X., Huang, Z., Ding, M., & Zhang, X. (2021). Deep learning based data-adaptive descriptor for non-rigid multimodal medical image registration. Signal Processing, 183(6), 108023. Search in Google Scholar

[22] Hermessi, H., Mourali, O., & Zagrouba, E. (2021). Multimodal medical image fusion review: theoretical background and recent advances - sciencedirect. Signal Processing. Search in Google Scholar

[23] Bennai, M., Guessoum, Z., Mazouzi, S., Cormier, S., & Mezghiche, M. (2023). Multi-agent medical image segmentation: a survey. Computer methods and programs in biomedicine, 232, 107444. Search in Google Scholar