Title :
Pattern Recognition of Lower Member Skin Ulcers in Medical Images with Machine Learning Algorithms
Author :
Seixas, Jose Luis ; Barbon, Sylvio ; Gomes Mantovani, Rafael
Author_Institution :
Univ. Estadual de Londrina - UEL, Londrina, Brazil
Abstract :
Misleading diagnosis of skin diseases may result in complications during the healing process. Skin images provide an important contribution to medical staff on storing and exchanging information to try preventing misdiagnosis. For such, image segmentation process may benefit from use of machine learning techniques, increasing simplicity of procedure, reducing computational costs and improving the diagnosis. This paper presents a comparison among different paradigms of machine learning to validate the segmentation of medical images of lower members ulcers, this segmentation allows wound pattern recognition to determinate injury region aiming at reducing the subjectivity of human evaluation.
Keywords :
biomedical optical imaging; diseases; image classification; image segmentation; injuries; learning (artificial intelligence); medical image processing; skin; wounds; healing process; human evaluation; image segmentation; information exchange; information storage; injury region; lower member skin ulcers; machine learning algorithms; medical images; medical staff; pattern recognition; skin disease diagnosis; wound pattern recognition; Accuracy; Biomedical imaging; Image color analysis; Image segmentation; Radio frequency; Support vector machines; Wounds; Image segmentation; Machine learning classifiers; Medical images; Pattern recognition;
Conference_Titel :
Computer-Based Medical Systems (CBMS), 2015 IEEE 28th International Symposium on
Conference_Location :
Sao Carlos
DOI :
10.1109/CBMS.2015.48