ABSTRACT
Semantic segmentation has been successfully explored in biological
studies to handle various applications, such as identifying wounds.
This study explores two image segmentation approaches to identify
mice wounds, specifically the U-Net and Random Forest algorithms.
The latter was combined with features extracted from the first two
layers of VGG16, which was used as a feature extractor. Experiments
were performed with a real dataset developed by the Pain,
Neuropathy, and Inflammation Laboratory at the State University
of Londrina with the approval of the University Ethics Committee
on Animal Research and Welfare. The experimental results were
promising, showing that both alternatives can provide accurate
predictions for most images regarding FScore and IoU evaluation
measures. Statistical tests were also applied, showing that U-Net
obtained statistically better results with an average FScore of 0.72
and IoU of 0.58.