Ovarian Cancer Detection Using Image Denoising And Enhanced 3D-Unet Segmentation
Keywords:
Biomedical imaging, 3D U-Net segmentation, Image quality improvement. Image denoising, Ovarian cancer detectionAbstract
This paper presents a novel approach for detecting ovarian cancer through image denoising and enhanced 3D U-Net segmentation. The proposed method incorporates an Enduring Noise Elimination Neural Network (ENEN) model architecture designed to encode input images into a lower-dimensional latent space and reconstruct the denoised images. This denoising process is crucial for improving the quality of medical images, which often suffer from noise due to various factors during acquisition. The denoised images are then subjected to segmentation, which partitions the image into multiple segments or regions to identify and delineate distinct objects or structures. Our segmentation approach utilizes the power of a 3D U-Net architecture, which extends the traditional 2D U-Net into three dimensions to handle volumetric data. The 3D U-Net is particularly effective in biomedical image segmentation tasks, making it an ideal choice for segmenting ovarian cancer regions in 3D medical scans. The model is trained to minimize the difference between the reconstructed image and the ground truth clean image during the denoising phase and then to accurately segment the denoised image. This method demonstrates significant improvements in the accuracy of ovarian cancer detection, highlighting the potential of combining advanced image denoising techniques with robust 3D segmentation architectures in medical imaging applications.
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Ahamad, M. M., Aktar, S., Uddin, M. J., Rahman, T., Alyami, S. A., Al-Ashhab, S., ...&Moni, M. A. (2022). Early-stage detection of ovarian cancer based on clinical data using machine learning approaches. Journal of personalized medicine, 12(8), 1211.
Boehm, K. M., Aherne, E. A., Ellenson, L., Nikolovski, I., Alghamdi, M., Vázquez-García, I., ...& Shah, S. P. (2022). Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. Nature cancer, 3(6), 723-733.
Cho, H. W., Cho, H., Kim, J., Kim, S., Lee, S., Song, J. Y., ...& Lee, J. K. (2024). Pelvic ultrasound-based deep learning models for accurate diagnosis of ovarian cancer: retrospective multicenter study.
Farahani, H., Boschman, J., Farnell, D., Darbandsari, A., Zhang, A., Ahmadvand, P., ...&Bashashati, A. (2022). Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images. Modern Pathology, 35(12), 1983-1990.
Ghoniem, R. M., Algarni, A. D., Refky, B., &Ewees, A. A. (2021). Multi-modal evolutionary deep learning model for ovarian cancer diagnosis. Symmetry, 13(4), 643.
Guo, L. Y., Wu, A. H., Wang, Y. X., Zhang, L. P., Chai, H., & Liang, X. F. (2020). Deep learning-based ovarian cancer subtypes identification using multi-omics data. BioData Mining, 13, 1-12.
Jung, Y., Kim, T., Han, M. R., Kim, S., Kim, G., Lee, S., & Choi, Y. J. (2022). Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder. Scientific Reports, 12(1), 17024.
Kim, M., Chen, C., Wang, P., Mulvey, J. J., Yang, Y., Wun, C., ...& Heller, D. A. (2022). Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning. Nature biomedical engineering, 6(3), 267-275.
Octaviani, T. L., Rustam, Z., &Siswantining, T. (2019, June). Ovarian Cancer Classification using Bayesian Logistic Regression. In IOP Conference Series: Materials Science and Engineering (Vol. 546, No. 5, p. 052049). IOP Publishing.
Saida, T., Mori, K., Hoshiai, S., Sakai, M., Urushibara, A., Ishiguro, T., ...& Nakajima, T. (2022). Diagnosing ovarian cancer on MRI: a preliminary study comparing deep learning and radiologist assessments. Cancers, 14(4), 987.
Schwartz, D., Sawyer, T. W., Thurston, N., Barton, J., &Ditzler, G. (2022). Ovarian cancer detection using optical coherence tomography and convolutional neural networks. Neural Computing and Applications, 34(11), 8977-8987.
Sengupta, D., Ali, S. N., Bhattacharya, A., Mustafi, J., Mukhopadhyay, A., &Sengupta, K. (2022). A deep hybrid learning pipeline for accurate diagnosis of ovarian cancer based on nuclear morphology. PloS one, 17(1), e0261181.
Taleb, N., Mehmood, S., Zubair, M., Naseer, I., Mago, B., & Nasir, M. U. (2022, February). Ovary cancer diagnosing empowered with machine learning. In 2022 International Conference on Business Analytics for Technology and Security (ICBATS) (pp. 1-6). IEEE.
Urase, Y., Nishio, M., Ueno, Y., Kono, A. K., Sofue, K., Kanda, T., ...& Murakami, T. (2020). Simulation study of low-dose sparse-sampling ct with deep learning-based reconstruction: usefulness for evaluation of ovarian cancer metastasis. Applied Sciences, 10(13), 4446.
Wang, C. W., Lee, Y. C., Chang, C. C., Lin, Y. J., Liou, Y. A., Hsu, P. C., ... & Chao, T. K. (2022). A weakly supervised deep learning method for guiding ovarian cancer treatment and identifying an effective biomarker. Cancers, 14(7), 1651.
Wu, M., Yan, C., Liu, H., & Liu, Q. (2018). Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks. Bioscience reports, 38(3), BSR20180289.
Yang, Z., Zhang, Y., Zhuo, L., Sun, K., Meng, F., Zhou, M., & Sun, J. (2024). Prediction of prognosis and treatment response in ovarian cancer patients from histopathology images using graph deep learning: a multicenter retrospective study. European Journal of Cancer, 199, 113532.
Yesilkaya, B., Perc, M., &Isler, Y. (2022). Manifold learning methods for the diagnosis of ovarian cancer. Journal of Computational Science, 63, 101775.
Zhang, Z., & Han, Y. (2020). Detection of ovarian tumors in obstetric ultrasound imaging using logistic regression classifier with an advanced machine learning approach. IEEE Access, 8, 44999-45008.
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