COMPUTATIONAL ANALYSIS AND ENHANCEMENT OF RADIOLOGICAL IMAGING

Radiological image processing is one of the most important areas in modern medical diagnostics. This field aims to improve the quality of medical images obtained from various radiological modalities (e.g., X-ray, CT, MRI, mammography), extract information more clearly and accurately, and support doctors in more effective decision-making. Image processing methods perform functions such as noise removal, contrast enhancement, edge detection, object segmentation, and classification. The main techniques used in radiological image processing include algorithms such as filtering, histogram equalization, morphological transformations, and Fourier transformation. These methods allow radiologists to more accurately analyze intra-organ structural changes. In recent years, new methods have been developed aimed at automatic image analysis and increasing diagnostic accuracy with the application of artificial intelligence and machine learning models. Deep learning systems based on neural networks in particular are effective in detecting tumors, anomalies, and tissue changes. Radiological image processing not only forms the technological basis of medical decision-making, but also plays an indispensable role in ensuring patient safety and early diagnosis. For this reason, the optimization, standardization, and integration of image processing systems with artificial intelligence technologies is considered one of the future development directions of medical science. Keywords: radiological image processing, medical diagnostics, artificial intelligence, machine learning, deep learning, segmentation.