Medical image analysis with Deep Learning approach

Medical image processing faces ongoing challenges in maintaining consistency and quality across healthcare facilities, with image degradation frequently occurring due to noise and artifacts. One of reasons why so is because of the image formats in use, particularly raster images such as .jpeg, .png etc. Raster images work on pixel based values, distorting data by a large amount when compressed or scaled-up. Quality of images often depends on device specifications and pixel values and hence, can vary from device-to-device. On the other hand, another format, Vector images such as .svg, .wmf, etc are prone to these compression errors due to a simple feature called anchor point maneuver. Vector graphics are connected by lines and curves (vectors) instead of pixel values, that help in preserving the data to a large extent when compressed or scaled up. This helps in removing the loss and preserving the quality of image. It is device independent and hence, can be used anywhere without losing its quality. While raster formats are the most widely used digital image processing formats, as highlighted they have inherent limitations in scaling operations, which results in quality degradation during compression. In contrast, vector image formats offer scalability without quality loss, facilitating more efficient editing capabilities.This paper proposes a novel technique to digital image processing that use the top-hat transformation algorithm to maintain image integrity during compression and scaling operations by converting the original raster images to vector images. The methodology includes deep learning integration, which enables lossless conversion operations. This study strengthens the field of AI-enabled healthcare by developing reliable strategies for preserving image consistency across the processing pipeline.