Deep learning based photo acoustic imaging for non-invasive imaging

Biomedical image processing is a technique for graphically representing inside human organs and tissues in addition to assessing them clinically. Artificial Intelligence (AI) approaches are used as they are capable of extracting complex data from picture data and providing a quantitative evaluation of radiographic features. The objectives of this paper would be to use deep learning techniques to generate noise artefacts in a photo acoustic image dataset, train a convolution neural network to identify and classify artefacts in photo acoustic data, and deploy an effective artefact elimination strategy to collected data. Present technology can be used to achieve the desired outcome using state-of-the-art image and data processing technologies as elements of AI and ML approaches. These technologies have given rise to a range of instruments that aid in better knowledge of the human body and the development of new diagnostic and therapeutic procedures, such as remote patient monitoring and treatment outcomes analysis, in terms of improving living and saving lives. During the testing phase, the suggested model for adaptive segregation and differentiation of noise and relevant data performed well in methodically segregating and distinguishing between noise and important data. The stage of training the model and collecting data takes longer, since the model must be taught with a diverse dataset that includes artefacts with faults in order for the model to identify them. When the noise and extraneous data are removed, the model's time to detect the artefacts or features of an image with noise is 1.735 ms to 2 s per picture per dataset, which is roughly 1.375 ms to 1.26 s faster than the previously reported time.

Automated summary

Biomedical image processing is a technique that uses artificial intelligence to extract complex data from internal human organs and evaluate them. This paper aims to use deep learning techniques to generate and classify artefacts in photo acoustic image datasets, and implement an effective artefact elimination strategy. These technologies have the potential to improve understanding of the human body and advance diagnostic and therapeutic procedures.