Assessment of the low-field magnetic resonance imaging for the brain scan imaging of the infant hydrocephalus.

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.

Optimized S-Curve Transformation and Wavelets-Based Fusion for Contrast Elevation of Breast Tomograms and Mammograms.

For the purpose of accuracy in detection and diagnosis, Computer-Aided Diagnosis (CAD) is preferred by radiologists for the analysis of Breast Cancer. However, the presence of noise, artifacts, and poor contrast in breast images during acquisition highlights the need for sophisticated enhancement techniques for the proper visualization of region-of-interest (ROI). In this work, contrast elevation of breast mammographic and tomographic images is performed with an improved S-Curve transform using the Particle Swarm Optimization (PSO) algorithm.