On the use of multi-objective evolutionary classifiers for breast cancer detection

• Published in: PloS one Vol. 17; no. 7; p. e0269950
• Main Authors: Dioşan, Laura, Andreica, Anca, Voiculescu, Irina
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.07.2022; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Annotations; Automation; Breast cancer; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - genetics; Cancer; Classification; Classifiers; Computer and Information Sciences; Data points; Databases, Factual; Deep learning; Diagnosis; Empirical analysis; Engineering and Technology; Evolutionary algorithms; Female; Flaw detection; Genetic algorithms; Histograms; Humans; Image classification; Mammography; Medical diagnosis; Medical imaging; Medicine and Health Sciences; Physical Sciences; Research and Analysis Methods; Statistical analysis; Statistical tests; Tumors; Romania
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0269950

Breast cancer is one of the most common tumours in women, nevertheless, it is also one of the cancers that is most usually treated. As a result, early detection is critical, which can be accomplished by routine mammograms. This paper aims to describe, analyze, compare and evaluate three image descriptors involved in classifying breast cancer images from four databases. Multi-Objective Evolutionary Algorithms (MOEAs) prove themselves as being efficient methods for selection and classification problems. This paper aims to study combinations of well-known classification objectives in order to compare the results of their application in solving very specific learning problems. The experimental results undergo empirical analysis which is supported by a statistical approach. The results are illustrated on a collection of medical image databases, but with a focus on the MOEAs' performance in terms of several well-known measures. The databases were chosen specifically to feature reliable human annotations, so as to measure the correlation between the gold standard classifications and the various MOEA classifications. We have seen how different statistical tests rank one algorithm over the others in our set as being better. These findings are unsurprising, revealing that there is no single gold standard for comparing diverse techniques or evolutionary algorithms. Furthermore, building meta-classifiers and evaluating them using a single, favorable metric is both extremely unwise and unsatisfactory, as the impact is to skew the results. The best method to address these flaws is to select the right set of objectives and criteria. Using accuracy-related objectives, for example, is directly linked to maximizing the number of true positives. If, on the other hand, accuracy is chosen as the generic metric, the primary classification goal is shifted to increasing the positively categorized data points.