Malaria parasite segmentation using U-Net: Comparative study of loss functions

Julisa Bana Abraham

doi:10.21924/cst.4.2.2019.128

Abstract

The convolutional neural network is commonly used for classification. However, convolutional networks can also be used for semantic segmentation using the fully convolutional network approach. U-Net is one example of a fully convolutional network architecture capable of producing accurate segmentation on biomedical images. This paper proposes to use U-Net for Plasmodium segmentation on thin blood smear images. The evaluation shows that U-Net can accurately perform Plasmodium segmentation on thin blood smear images, besides this study also compares the three loss functions, namely mean-squared error, binary cross-entropy, and Huber loss. The results show that Huber loss has the best testing metrics: 0.9297, 0.9715, 0.8957, 0.9096 for F1 score, positive predictive value (PPV), sensitivity (SE), and relative segmentation accuracy (RSA), respectively.

Highlights

Deep learning is a compelling and versatile method
Deep convolutional networks are one of deep learning architectures designed for the areas of computer vision and image processing, convolutional networks first proposed by [2] and began to receive attention in the world after Alexnet [3] won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [4] which is an image classification competition using 8 layers of convolutional neural networks (CNN) that are trained using graphics processing units (GPU)
CNN is commonly used for classification, convolutional networks can be used for semantic segmentation using the fully convolutional network [5] approach

Summary

Introduction

Deep learning is a compelling and versatile method. Deep convolutional networks are one of deep learning architectures designed for the areas of computer vision and image processing, convolutional networks first proposed by [2] and began to receive attention in the world after Alexnet [3] won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [4] which is an image classification competition using 8 layers of convolutional neural networks (CNN) that are trained using graphics processing units (GPU). U-Net [6] is one example of fully convolutional network architecture capable of producing accurate segmentation on biomedical images, U-Net is designed explicitly for biomedical image segmentation and gets the highest intersection over union (IoU) for ISBI cell tracking challenge 2015. Due to the excellent U-Net performance for biomedical segmentation, this paper tries to implement U-Net to segment Malaria parasite or Plasmodium on thin blood smears

Objectives

Methods

Results