Deep Learning-based Segmentation and Computer Vision-based Ultrasound Imagery Techniques

Abstract

Biomedical imaging has been a game-changer in the medical field because of how it facilitates the analysis of human body issues. The best possible diagnosis may now be made thanks to the development of computer science and its integration with medical imaging. The state-of-the-art in biological image processing has been demonstrated by methods based on deep learning. Thanks to these approaches' inherent capacity for self-learning, manually constructed features are no longer necessary. It has given a world-class solution with trustworthy outcomes for medical image processing in this way. This effort is being done to help with better diagnosis by addressing the key challenges mentioned above. These approaches' inherent capacity for self-learning has rendered custom enhancements superfluous. This has supplied an excellent solution with trustworthy outcomes in the field of medical image processing. In order to better diagnose health problems, the current effort is conducted to solve the aforementioned key challenges. Therefore, the study aims to create novel segmentation and classification methods for identifying the different types of breast masses from US pictures. The elastic characteristics of tissues can now be imaged thanks to a novel method called elastography. In our studies, we use ultrasound elastography as well as ultrasound B-mode to categorise breast tumours.