Super-resolution using 3d convolutional neural networks in CT scan image of COVID19
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Abstract
In the medical field, the accuracy and quality of the image led to a rapid increase in using deep learning models-based architectures convolutional nerve CNN where three-dimensional convolutional neural networks (3D) CNNs utilized to analyze the medical images. This paper provides the pre-processing steps for the medical images then fed to the CNNs 3D. The objective of the proposed model is to operate from low-resolution to reach high-resolution images by using Keras with TensorFlow as wallpaper for training; this training is work separately on the different scaling factors for reconstruction. Two datasets of lung tomography data are used in this paper, the first dataset is Kaggle.coved2020 for Corona patients, and the second is the Iraqi dataset for Corona patients 2020. The proposal method that demonstrated improvements over the bicubic upsampling and FSRCNN peak signal-to-noise ratio (PSNR) 37.10 dB with 40.47 dB respectively.
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