An Evaluation of Medical Image Analysis Using Image Segmentation and Deep Learning Techniques

Abstract

Segmenting images is a subfield of digital image processing that has several uses in areas like as image analysis, augmented reality, machine vision, and many more. As medical image analysis develops as a profession, the task of segmenting organs, illnesses, or anomalies in these pictures becomes more challenging. Because there are so many different types of artefacts in medical photos, segmenting them is a tremendous challenge. There have been several recent examples of deep learning (DL) models being used for picture segmentation. Medical picture segmentation using DL techniques is the subject of this work's comparative investigation. Utilising two separate datasets, improved the image quality by thorough preprocessing. One dataset consisted of extensive lung CT scans, while the other was a chest X-ray dataset. These datasets were selected to reflect different medical imaging issues. Image improvement using grayscaling and histogram equalization are part of our process. Splitting the datasets into separate groups for training and testing made model deployment much easier. The results of this study show that both UNet++ and VGG16 are effective; however, VGG16 achieved 99% accuracy in analyzing chest X-ray images, while UNet++ 98% was much better at segmenting lung images. Limitations of this study include possible biases in the dataset and the necessity for additional validation on bigger datasets, however it does offer useful insights. To be sure these deep learning models can handle medical image processing, future studies should look at ensemble methods, make the models easier to understand, and test their results in real-world contexts.