Training artificial neural networks using self-organizing migrating algorithm for skin segmentation

• Published in: Scientific reports Vol. 14; no. 1; pp. 22651 - 13
• Main Authors: Diep, Quoc Bao, Truong, Thanh-Cong, Zelinka, Ivan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 30.09.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/705; Accuracy; Algorithms; Artificial neural networks; Back propagation; Computer engineering; Computer science; Computer vision; Datasets; Deep learning; Experiments; Genetic algorithms; Heuristic; Humanities and Social Sciences; Humans; Image processing; Image Processing, Computer-Assisted - methods; Methods; Migration; multidisciplinary; Neural networks; Neural Networks, Computer; Optimization algorithm; Optimization algorithms; Optimization techniques; Propagation; Science; Science (multidisciplinary); Skin; Skin - diagnostic imaging; Skin segmentation; SOMA; Swarm intelligence; Training; Water quality; Computer vision; Optimization algorithm; Swarm intelligence; Skin segmentation; SOMA; Artificial neural networks
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-72884-0

This study presents an application of the self-organizing migrating algorithm (SOMA) to train artificial neural networks for skin segmentation tasks. We compare the performance of SOMA with popular gradient-based optimization methods such as ADAM and SGDM, as well as with another evolutionary algorithm, differential evolution (DE). Experiments are conducted on the skin dataset, which consists of 245,057 samples with skin and non-skin labels. The results show that the neural network trained by SOMA achieves the highest accuracy (93.18%), outperforming ADAM (84.87%), SGDM (84.79%), and DE (91.32%). The visual evaluation also reveals the SOMA-trained neural network’s accurate and reliable segmentation capabilities in most cases. These findings highlight the potential of incorporating evolutionary optimization algorithms like SOMA into the training process of artificial neural networks, significantly improving performance in image segmentation tasks.