MediColor: Enhancing Grayscale Medical Image Diagnosis and Anomaly Detection

Abstract

"In healthcare, diagnosing medical images is crucial but often challenged by the poor quality of grayscale images, which lack the clarity and contrast needed to detect fine details and anomalies essential for accurate diagnoses. This research addresses these limitations by implementing pseudo-coloring techniques, image enhancement filters, and predictive anomaly detection to improve the interpretability and diagnostic value of grayscale medical images.

Pseudo-coloring was used to add color to grayscale images, enhancing contrast and making key features more visible. Custom filters were applied to reduce noise and sharpen edges, improving image clarity. Predictive anomaly detection algorithms identified areas of concern, aiding in more comprehensive image analysis.

The study demonstrated significant improvements in image quality and diagnostic accuracy, as shown by increased PSNR and SSIM values, which measure contrast and clarity. These findings suggest that pseudo-coloring and image restoration techniques have the potential to transform medical image diagnosis, ultimately improving patient outcomes."