Fine-tuning U-net for medical image segmentation based on activation function, optimizer and pooling layer

Abstract

U-net convolutional neural network (CNN) is a famous architecture developed to deal with medical images. Fine-tuning CNNs is a common technique used to enhance their performance by selecting the building blocks which can provide the ultimate results. This paper introduces a method for tuning U-net architecture to improve its performance in medical image segmentation. The experiment is conducted using an x-ray image segmentation approach. The performance of U-net CNN in lung x-ray image segmentation is studied with different activation functions, optimizers, and pooling-bottleneck-layers. The analysis focuses on creating a method that can be applied for tuning U-net, like CNNs. It also provides the best activation function, optimizer, and pooling layer to enhance U-net CNN’s performance on x-ray image segmentation. The findings of this research showed that a U-net architecture worked supremely when we used the LeakyReLU activation function and average pooling layer as well as RMSProb optimizer. The U-net model accuracy is raised from 89.59 to 93.81% when trained and tested with lung x-ray images and uses the LeakyReLU activation function, average pooling layer, and RMSProb optimizer. The fine-tuned model also enhanced accuracy results with three other datasets.