Image processing and deep learning for colorectal cancer diagnosis

Artificial Intelligence, smart applications, machine learning, and the Internet of Things, are now revolutionizing virtually every aspect of our lives. Healthcare applications are currently one of the most promising areas for machine learning specialists, with emerging approaches that aim to moderni...

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Κύριοι συγγραφείς:	Μουλίτα, Φράντσεσκ, Mulita, Francesk
Άλλοι συγγραφείς:	Αναγνωστόπουλος, Χρήστος
Γλώσσα:	English
Δημοσίευση:	2025
Θέματα:	artificial intelligence machine learning smart applications Artificial intelligence Deep learning (Machine learning)
Διαθέσιμο Online:	http://hdl.handle.net/11610/26976
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author	Μουλίτα, Φράντσεσκ Mulita, Francesk
author2	Αναγνωστόπουλος, Χρήστος
author_facet	Αναγνωστόπουλος, Χρήστος Μουλίτα, Φράντσεσκ Mulita, Francesk
author_sort	Μουλίτα, Φράντσεσκ
collection	DSpace
description	Artificial Intelligence, smart applications, machine learning, and the Internet of Things, are now revolutionizing virtually every aspect of our lives. Healthcare applications are currently one of the most promising areas for machine learning specialists, with emerging approaches that aim to modernize medical practice. This thesis aims to introduce readers to the latest technological advances in the field of deep learning, and more specifically its applications in the diagnosis and surgical management of colorectal cancer. Colorectal cancer is used as a template condition throughout the book, largely due to its nature as one of the most well-studied types of malignancy; it offers a unique combination of high prevalence and radical treatment potential through surgical approaches. The applications presented here, include studied and tested methods, many of which have already found their way into everyday surgical practice. This book aims to be a valuable resource for all healthcare researchers, AI-specialized computer scientists, surgical trainees, and clinicians alike. We have strived to outline the latest and most promising advances of AI in surgical practice while emphasizing why physicians still have to play a central, pivotal role in rolling out such advances. AI is expected to define the future of personalized healthcare delivery and enhance patient safety and satisfaction.
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institution	Hellanicus
language	English
publishDate	2025
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spelling	oai:hellanicus.lib.aegean.gr:11610-269762025-03-14T08:14:42Z Image processing and deep learning for colorectal cancer diagnosis Επεξεργασία εικόνας και βαθιά μάθηση για την διάγνωση του κολοορθικού καρκίνου Μουλίτα, Φράντσεσκ Mulita, Francesk Αναγνωστόπουλος, Χρήστος Ευφυή Συστήματα Πληροφορικής artificial intelligence machine learning smart applications Artificial intelligence Deep learning (Machine learning) Artificial Intelligence, smart applications, machine learning, and the Internet of Things, are now revolutionizing virtually every aspect of our lives. Healthcare applications are currently one of the most promising areas for machine learning specialists, with emerging approaches that aim to modernize medical practice. This thesis aims to introduce readers to the latest technological advances in the field of deep learning, and more specifically its applications in the diagnosis and surgical management of colorectal cancer. Colorectal cancer is used as a template condition throughout the book, largely due to its nature as one of the most well-studied types of malignancy; it offers a unique combination of high prevalence and radical treatment potential through surgical approaches. The applications presented here, include studied and tested methods, many of which have already found their way into everyday surgical practice. This book aims to be a valuable resource for all healthcare researchers, AI-specialized computer scientists, surgical trainees, and clinicians alike. We have strived to outline the latest and most promising advances of AI in surgical practice while emphasizing why physicians still have to play a central, pivotal role in rolling out such advances. AI is expected to define the future of personalized healthcare delivery and enhance patient safety and satisfaction. 2025-01-21T09:07:06Z 2025-01-21T09:07:06Z 2023-02-10 http://hdl.handle.net/11610/26976 en Default License 138 σ. application/pdf Μυτιλήνη
spellingShingle	artificial intelligence machine learning smart applications Artificial intelligence Deep learning (Machine learning) Μουλίτα, Φράντσεσκ Mulita, Francesk Image processing and deep learning for colorectal cancer diagnosis
title	Image processing and deep learning for colorectal cancer diagnosis
title_full	Image processing and deep learning for colorectal cancer diagnosis
title_fullStr	Image processing and deep learning for colorectal cancer diagnosis
title_full_unstemmed	Image processing and deep learning for colorectal cancer diagnosis
title_short	Image processing and deep learning for colorectal cancer diagnosis
title_sort	image processing and deep learning for colorectal cancer diagnosis
topic	artificial intelligence machine learning smart applications Artificial intelligence Deep learning (Machine learning)
url	http://hdl.handle.net/11610/26976
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Image processing and deep learning for colorectal cancer diagnosis

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