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• W3189553628 startingPage "293" @default.
• W3189553628 abstract "Deep learning is slowly taking over the medical image analysis field with advancements in imaging tools, and growing demand for fast, accurate, and automated image analysis. In this chapter we have extensively reviewed the field from inception to its current state-of-the-art techniques. We begin by introducing imaging advancements over the decades, followed by general pre-analysis processes. Further, we describe the different kinds of deep learning models used for image analysis, after broadly classifying them into supervised and unsupervised learning. Here the chapter touches upon the architecture and complex functionality of each layer of such a deep neural network. CNN being one of the most popular tools for image analysis is then reviewed in-depth. We have discussed its architecture and learning along with the mathematical framework behind it, followed by real-world applications. The second half of this chapter is devoted to the biological applications of deep learning. We start by reviewing advancements from sub-cellular to cellular to the tissue level, ending with an extensive review at the organ level. We have included various deep learning tools and techniques that people have attempted to better understand the morphology and physiology of the human body, and its related diseases. Finally, the chapter ends with current field-specific challenges and the ways forward to eliminate them." @default.
• W3189553628 created "2021-08-16" @default.
• W3189553628 creator A5031309167 @default.
• W3189553628 creator A5035213829 @default.
• W3189553628 creator A5084364240 @default.
• W3189553628 date "2021-08-06" @default.
• W3189553628 modified "2023-10-12" @default.
• W3189553628 title "Deep Learning Applications in Medical Image Analysis" @default.
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