Use este identificador para citar ou linkar para este item: http://repositorio.ufc.br/handle/riufc/73437
Tipo: Artigo de Periódico
Título: Computer-Aided ankle ligament injury diagnosis from magnetic resonance images using machine learning techniques
Autor(es): Silva, Daniel Santos da
Nascimento, Caio dos Santos
Damaševičius, Robertas
Jagatheesaperumal, Senthil Kumar
Albuquerque, Victor Hugo Costa de
Leite, Jose Alberto Dias
Astolfi, Rodrigo Schroll
Guedes, Ingrid S.
Palavras-chave: Ankle ligament injury;MRI;Data augmentation;Feature extraction;Lesão ligamentar do tornozelo;Aumento de dados;Extração de recursos;Ressonância magnética
Data do documento: 2023
Instituição/Editor/Publicador: Sensors
Citação: SILVA, Daniel Santos da; NASCIMENTO, Caio dos Santos; DAMAŠEVIČIUS, Robertas; JAGATHEESAPERUMAL, Senthil Kumar; ALBUQUERQUE, Victor Hugo Costa de; LEITE, Jose Alberto Dias; ASTOFI, Rodrigo Schroll; GUEDES, Ingrid S.. Computer-Aided ankle ligament injury diagnosis from magnetic resonance images using machine learning techniques. Sensors, [s.l.], v. 23, n. 3, p. 1565, 2023.
Abstract: Ankle injuries caused by the Anterior Talofibular Ligament (ATFL) are the most common type of injury. Thus, finding new ways to analyze these injuries through novel technologies is critical for assisting medical diagnosis and, as a result, reducing the subjectivity of this process. As a result, the purpose of this study is to compare the ability of specialists to diagnose lateral tibial tuberosity advancement (LTTA) injury using computer vision analysis on magnetic resonance imaging (MRI). The experiments were carried out on a database obtained from the Vue PACS–Carestream software, which contained 132 images of ATFL and normal (healthy) ankles. Because there were only a few images, image augmentation techniques was used to increase the number of images in the database. Following that, various feature extraction algorithms (GLCM, LBP, and HU invariant moments) and classifiers such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were used. Based on the results from this analysis, for cases that lack clear morphologies, the method delivers a hit rate of 85.03% with an increase of 22% over the human expert-based analysis.
URI: http://www.repositorio.ufc.br/handle/riufc/73437
ISSN: 1424-8220
Tipo de Acesso: Acesso Aberto
Aparece nas coleções:DEEL - Artigos publicados em revista científica

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