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Title: A Deep Learning approach to Case Based Reasoning to the Evaluation and Diagnosis of Cervical Carcinoma
Authors: Neves, José
Vicente, Henrique
Ferraz, Filipa
Leite, Ana Catarina
Rodrigues, Ana Rita
Cruz, Manuela
Machado, Joana
Neves, João
Sampaio, Luzia
Keywords: Artificial Intelligence
Deep Learning
Machine Learning
Cervical Carcinoma
Magnetic Resonance Imaging
Logic Programming
Knowledge Representation and Reasoning
Case Based Reasoning
Issue Date: 2018
Publisher: Springer International Publishing
Citation: Neves, J., Vicente, H., Ferraz, F., Leite, A.C., Rodrigues, A.R., Cruz, M., Machado, J., Neves, J. & Sampaio, L., A Deep Learning approach to Case Based Reasoning to the Evaluation and Diagnosis of Cervical Carcinoma. Studies in Computational Intelligence, 769: 185–197, 2018.
Abstract: Deep Learning (DL) is a new area of Machine Learning research introduced with the objective of moving Machine Learning closer to one of its original goals, i.e., Artificial Intelligence (AI). DL breaks down tasks in ways that makes all kinds of machine assists seem possible, even likely. Better preventive healthcare, even better recommendations, are all here today or on the horizon. However, keeping up the pace of progress will require confronting currently AI’s serious limitations. The last but not the least, Cervical Carcinoma is actuality a critical public health problem. Although patients have a longer survival rate due to early diagnosis and more effective treatment, this disease is still the leading cause of cancer death among women. Therefore, the main objective of this article is to present a DL approach to Case Based Reasoning in order to evaluate and diagnose Cervical Carcinoma using Magnetic Resonance Imaging. It will be grounded on a dynamic virtual world of complex and interactive entities that compete against one another in which its aptitude is judged by a single criterion, the Quality of Information they carry and the system’s Degree of Confidence on such a measure, under a fixed symbolic structure.
URI: chapter/
ISBN: 978-3-319-76080-3
ISSN: 1860-949X (paper)
1860-9503 (electronic)
Type: article
Appears in Collections:QUI - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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