降噪
人工智能
计算机科学
预处理器
深度学习
规范化(社会学)
卷积神经网络
医学诊断
模式识别(心理学)
自编码
医学影像学
噪音(视频)
人工神经网络
机器学习
计算机视觉
图像(数学)
放射科
医学
社会学
人类学
作者
Mohammad El Zein,Wissam El Laz,Malak Laza,Taha Wazzan,Ibrahim Kaakour,Yasmine Abu Adla,Jad Baalbaki,Mohammad O. Diab,Maher Sabbah,Rached Zantout
标识
DOI:10.1109/biosmart58455.2023.10162068
摘要
Magnetic Resonance Imaging (MRI) has become an indispensable tool in the medical field for diagnosing and monitoring various diseases and conditions. However, the quality of MRI images can be degraded by noise, which can lead to inaccurate interpretations and diagnoses. In recent years, machine learning techniques have shown great potential in enhancing the accuracy of image denoising, especially in the medical domain. In this study, we propose a novel deep learning model based on autoencoders for denoising MRI images. We employed various preprocessing techniques such as data augmentation, resizing, normalization, and conventional denoising methods on our MRI images. Our model comprises a convolutional neural network autoencoder (CCNAE), which we fine-tuned by testing different parameters and layers to achieve optimal performance. Our results demonstrate a validation loss of approximately 0.0001, indicating a substantial improvement in denoising performance. Our work represents an important step towards developing an efficient and effective method for denoising MRI images without compromising critical data or time.
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