Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography

Autor: Adriel dos Santos Araujo, Débora C. Muchaluat-Saade, Aura Conci, Lincoln Faria da Silva, Petrucio R. T. Medeiros, Roger Resmini
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Computer science
diagnosis
Context (language use)
Breast Neoplasms
02 engineering and technology
TP1-1185
Biochemistry
Article
030218 nuclear medicine & medical imaging
Analytical Chemistry
03 medical and health sciences
0302 clinical medicine
Breast cancer
breast cancer
Genetic algorithm
Classifier (linguistics)
0202 electrical engineering
electronic engineering
information engineering
medicine
genetic algorithm
Humans
support vector machine
Temperature difference
Electrical and Electronic Engineering
Instrumentation
Receiver operating characteristic
business.industry
Chemical technology
Pattern recognition
medicine.disease
Atomic and Molecular Physics
and Optics
thermography
Support vector machine
ROC Curve
Thermography
020201 artificial intelligence & image processing
Female
Artificial intelligence
business
Algorithms
Zdroj: Sensors
Volume 21
Issue 14
Sensors, Vol 21, Iss 4802, p 4802 (2021)
Sensors (Basel, Switzerland)
ISSN: 1424-8220
DOI: 10.3390/s21144802
Popis: Breast cancer is one of the leading causes of mortality globally, but early diagnosis and treatment can increase the cancer survival rate. In this context, thermography is a suitable approach to help early diagnosis due to the temperature difference between cancerous tissues and healthy neighboring tissues. This work proposes an ensemble method for selecting models and features by combining a Genetic Algorithm (GA) and the Support Vector Machine (SVM) classifier to diagnose breast cancer. Our evaluation demonstrates that the approach presents a significant contribution to the early diagnosis of breast cancer, presenting results with 94.79% Area Under the Receiver Operating Characteristic Curve and 97.18% of Accuracy.
Databáze: OpenAIRE
Externí odkaz: