Lung Image Segmentation using Deep Learning: An Overview, Research Trends and Challenges

Abstract

In Healthcare 4.0, Lungs disease has become one of the deadliest infectious disease in society. Earlier medical diagnosis related to Lungs diseases takes a time lot to proper analysis and detection. Lungs disease detection system require more accurate in segmentation to help for treatments. Computer assisted techniques plays a vital role in modern medicines. DLTs          (Deep Learning Techniques) have stepped into many real world application areas. The rapid development of medical images using DLTs has become a major research area, which widely used in Healthcare. Generally, image interpretation by skilled humans are not in sufficient, because of its complexity, subjectivity, multifaceted nature of the image and broad varieties exist across various interpreters and exhaustion. It is necessary to analyze comprehensively how DL (Deep Learning) image segmentation to help in Healthcare sector. In this article several algorithms, tools, strengths and future challenges of Image segmentation in Healthcare sector discussed using DL techniques. Moreover, research issues are also discussed. This study will aid major researchers in understanding performance improvements in Medical Image segmentation using DLTs.