Deep learning for biomedical image analysis in place of fundamentals, limitations, and prospects of deep learning for biomedical image analysis

Clinical techniques used for the timely identification, observation, diagnostics, and therapy assessment of a wide range of medical problems are just a few examples of how biomedical imaging is crucial in these clinical applications. Grasping medical image analysis in computer vision requires a fundamental understanding of the ideas behind artificial neural networks and deep learning (DL), as well as how they are implemented. Due to its dependability and precision, DL is well-liked among academics and researchers, particularly in the engineering and medical disciplines. Early detection is a benefit of DL approaches in the realm of medical imaging for illness diagnosis. The simplicity and reduced complexity of DL approaches are their key characteristics, which eventually save time and money while tackling several difficult jobs at once. DL and artificial intelligence (AI) technologies have advanced significantly in recent years. In every application area, but particularly in the medical one, these methods are crucial. Examples include image analysis, image processing, image segmentation, image fusion, image registration, image retrieval, image-guided treatment, computer-aided diagnosis (CAD), and many more. This chapter seeks to thoroughly present DL methodologies and the potential for biological imaging utilizing DL, as well as explore problems and difficulties.