| Abstrakt: |
Medical Imaging (MI) plays a crucial role in healthcare, including disease diagnosis, treatment, and continuous monitoring. The integration of non-invasive techniques such as X-ray, Positron Emission Tomography (PET) scan, Computed Tomography (CT) scan, Magnetic Resonance Imaging (MRI), and Ultrasound, has significantly enhanced medical treatment. MI enables visualization of internal structures without invasive procedures, aiding in the diagnosis of various diseases. The introduction of Medical Image Processing (MIP) has further improved disease prediction, detection, analysis, and evaluation. MIP data is utilized in Machine Learning (ML) and Deep Learning (DL) models to develop intelligent systems that enhance medical assistance and better recognition, because human interpretation of medical images is error prone and exhaustive. However, accuracy is crucial for the provision of high-quality healthcare. This has motivated various works on MI using MIP therefore, this paper emphasizes how these imaging modalities can be used to analyze, model, and manipulate data in order to achieve maximum treatment outcome. Moreover, a comprehensive literature survey is conducted to provide a detailed analysis of the working principles, benefits, and limitations of diverse imaging modalities. It explores state-of-the-art methodologies rooted in MI approaches and highlights potential future developments, challenges, trends, observations, and significant improvements in the field. [ABSTRACT FROM AUTHOR] |
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