Open AccessCLASSIFICATION OF MEDICAL IMAGES USING MACHINE LEARNING
Author(s) -
Eduardo Pérez Careta,
J. R. Guzmán-Sepúlveda,
JOSE MERCED LOZANO GARCIA,
M. Torres-Cisneros,
Rafael Guzmán-Cabrera
Publication year - 2022
Publication title -
dyna
Language(s) - English
Resource type - Journals
eISSN - 1989-1490
pISSN - 0012-7361
DOI - 10.6036/10117
Subject(s) - artificial intelligence , computer science , machine learning , support vector machine , naive bayes classifier , centroid , convolutional neural network , classifier (uml) , artificial neural network , deep learning , pattern recognition (psychology)
The popularity of the use of computational tools such as artificial intelligence has been increasing in recent years, and its importance in medicine is a fact. This field has benefited greatly thanks to the incorporation of more effective and faster methodologies in the medical diagnosis and registration processes. In the present work, the classification of images related to three diseases: Tuberculosis, Glaucoma and Parkinson's is carried out. We used deep learning and the RESNET50 convolutional neural network to extract classification characteristics, and then perform the classification based on standard methods, such as support vector machines, Naïve Bayes, and Centroid-based classifier, which are incorporated into two scenarios (cross validation; training and test sets). The classifier's performance is evaluated quantitatively using three evaluation metrics. The results obtained support the feasibility of the proposed methodology and its potential to improve medical diagnosis.