Enhancing breast cancer diagnosis with deep learning and evolutionary algorithms: A comparison of approaches using different thermographic imaging treatments

• Published in: Multimedia tools and applications Vol. 83; no. 14; pp. 42955 - 42971
• Main Authors: Nogales, Alberto, Pérez-Lara, Fernando, García-Tejedor, Álvaro J.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Breast cancer; Computer Communication Networks; Computer Science; Computer vision; Data Structures and Information Theory; Deep learning; Diagnosis; Evolutionary algorithms; Knowledge management; Machine learning; Medical imaging; Multimedia Information Systems; Special Purpose and Application-Based Systems; Thermography; Track 2: Medical Applications of Multimedia; Deep learning; Evolutive algorithm; Medical imaging; Convolutional neural networks; Breast cancer diagnosis; Thermography
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-17281-x

  The medical field has come a long way in recent years. This fact is directly related to the application of computer science, particularly artificial intelligence. Computer vision is one of its applications with the most significant knowledge transfer to private companies or organizations. Due to the large number of tests based on images, it has multiple benefits in medical diagnosis. These benefits go from health to economics, passing through time savings. Most people know X-rays or scanners, but others have not been applied too much like thermographies. Although they are inexpensive, non-invasive, painless, and easy to implement in remote areas, their scientific evidence is not very extended. In this paper, we evaluate different approaches based on four use cases depending on which treatment we applied to the images. This step leads to various scenarios that could benefit from using advanced hybrid Artificial Intelligence models. Evaluating the solutions will not only provide us with an accurate model. Still, it will also allow us to understand further how the different thermograph information influences the diagnosis. Results show that by separating the thermography by three ranges of temperatures and using a hybrid model of convolutional neural networks and evolutive algorithms, we can achieve accuracy near 94%.