| Color conversion |
Color conversion (Ozaltin, Yeniay & Subasi, 2023), Pixel brightness transformations (Mirza, Siddiq & Khan, 2023), Brightness corrections (Ebenezer et al., 2022) |
| Normalization |
Standardization of SARS-CoV-2 image (Saiz & Barandiaran, 2020), Geometric Transformations, Mean Normalization improves CNN accuracy in SARS-CoV-2 detection (Yaman, Karakaya & Erol, 2022), Normalization of SARS-CoV-2 CT image for feature extraction (Pratiwi et al., 2021), SARS-CoV-2image histogram equalization by CLAHE (Al-Waisy et al., 2021), Histogram Equalization increases performance of learning of CNN in SARS-CoV-2 Detection (Maity, Nair & Chandra, 2020). Image Restoration, Standardization: Standardization of SARS-CoV-2 image improves model learning to get better performance (Saiz & Barandiaran, 2020). A learning pipeline used for the standardization of SARS-CoV-2 chest X-ray images for deep learning method (Wang et al., 2021a) and Z-Component analysis. |
| Filtering |
Smoothing (Karthik, Menaka & Hariharan, 2021), Edge Detection (Purohit et al., 2022), Sharpening (Purohit et al., 2022) Filtering CT (Deshpande & Schuller, 2020; Subramanian et al., 2022) and X ray image (Han, Yang & Lee, 2010; Shorten, Khoshgoftaar & Furht, 2021), Reshaping (Fouladi et al., 2021). |
| Segmentation |
Segmenting SARS-CoV-2 image (Yan et al., 2020; Wang et al., 2020a), Fourier transform of SARS-CoV-2 image in deep learning (Wang, Zhang & Zhang, 2021). To make the more clear and formatted image as machine can understand SARS-CoV-2 detection with fine pre-processed image using CT and xray like Edge-based Segmentation (Fan et al., 2020a) Threshold Segmentation (Voulodimos et al., 2021), Region-based Segmentation (Voulodimos et al., 2021), Clustering-based Segmentation (Tello-Mijares & Woo, 2021) and Watershed-based Segmentation (Ahsan et al., 2021). |
