Table 2.

Common Image Processing Techniques.

Image Processing Techniques	Approach	Applications
Segmentation: Object location and region partitioning29,32	Thresholding, boundary highlighting Random Forest, SVM, CNN, U-Net	Tumor detection, Organ Delineation Disease Diagnosis and Monitoring Treatment Planning
Classification: Image categorization into predefined classes and assigning labels.30,31	HOG, SIFT, kNN DenseNet, resNet Multimodality	Tissues type characterization. Disease classification. Health conditions classifying.
Reconstruction and Filtration: Reconstruct incomplete images, construct images from raw data and remove noise33,34	Filtered Back Projection, GAN, LSTM Spatial and Gaussian filter YOLO, DETR	Artifacts removal. Creating 3D MRI and CT images. Improve the quality of the image.
Augmentation: Improve model performance, and disparate training data generation.35	Elastic and Geometric transformation Generative Models Histogram equalisation, Intensity Adjustment	Enhance training dataset. Reduce overfitting. Emphasizes minimally invasive treatments.