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Objective: In today's global clinical settings, low-field magnetic resonance imaging (MRI) technology is becoming increasingly prevalent. Ensuring high-quality image acquisition is crucial for accurate disease diagnosis and treatment and for evaluating the impact of poor-quality images. In this study, we explored the potential of deep learning as a diagnostic tool for improving image quality in hydrocephalus analysis planning. This could include discussions on the diagnostic accuracy, cost-effectiveness, and practicality of using low-field MRI as an alternative. Methods: There are many reasons which are going to affect infant computed tomography images. These are spatial resolution, noise, and contrast between the brain and cerebrospinal fluid (CSF). Now, we can enhance using the application of deep learning algorithms. Both improved and down quality were situated to the three qualified pediatric neurosurgeons comfortable with working in poor- to middle-level income countries for the analysis of clinical tools in the planning of the treatment of hydrocephalus. Results: The results predict that a picture will be classified as beneficial for hydrocephalus treatment planning, according to image resolution and the contrast-to-noise ratio (CNR) between the brain and CSF. The CNR is significantly improved by deep learning enhancement, which also improves the apparent likelihood of the image. Conclusion: However, poor-quality images might be desirable to image improved by deep learning, since those images will not offer the risk of confusing facts which could misguide the decision of the analysis of patients. Such findings support the newly introduced measurement standards in estimating the acceptable quality of images for clinical use.
RESUMEN
OBJECTIVE: Content-based image retrieval (CBIR) is the most suitable and alternative method for older text searches that use keywords. This article aims to improve feature extraction as well as matching techniques designed for more accurate and precise CBIR systems, especially for brain scan images associated with various brain diseases and abnormalities. Tests should be described at an appropriate success rate. METHODS: Various methods of producing medical images are discussed, and examples of biological applications are given. The discussion emphasizes as an introduction to CBIR the new method of echo-planar imaging, which is fully described. We have done here many methods related to digital image processing and we had developed a code for retrieving everything automatically. This application has been developed in Matlab software. RESULTS: Testing the correctness and effectiveness of the system evolved becomes more important when the system is going to be used in real-time and more when it is for humankind, i.e., medical diagnosis. Nowadays, our science and technology areas as develop as we can say that we have such advanced medical equipment so that our thought and program can be capable that it is giving us useful results. Determining if whether the two images are identical or not, it depends on the point of view of the person. CONCLUSIONS: In this paper, the outcome of feature extraction and matching by setting cutoff limit and threshold is pretty promising. Further studies can be done apart from computed tomography scans for a more generalized CBIR system.
