Malaria Detection from Blood Cell Images Using XceptionNet

TL;DR

This paper demonstrates that deep convolutional neural networks, especially XceptionNet and Residual Attention Network, can accurately classify malaria-infected blood cell images, offering a reliable automated diagnostic tool with over 97% accuracy.

Contribution

The study compares multiple deep networks for malaria detection, highlighting the superior performance of XceptionNet and Residual Attention Network on a public dataset.

Findings

XceptionNet achieved 97.55% accuracy.

Residual Attention Network achieved 97.28% accuracy.

Deep learning models outperform traditional methods.

Abstract

Malaria, which primarily spreads with the bite of female anopheles mosquitos, often leads to death of people - specifically children in the age-group of 0-5 years. Clinical experts identify malaria by observing RBCs in blood smeared images with a microscope. Lack of adequate professional knowledge and skills, and most importantly manual involvement may cause incorrect diagnosis. Therefore, computer aided automatic diagnosis stands as a preferred substitute. In this paper, well-demonstrated deep networks have been applied to extract deep intrinsic features from blood cell images and thereafter classify them as malaria infected or healthy cells. Among the six deep convolutional networks employed in this work viz. AlexNet, XceptionNet, VGG-19, Residual Attention Network, DenseNet-121 and Custom-CNN. Residual Attention Network and XceptionNet perform relatively better than the rest on a publicly available malaria cell image dataset. They yield an average accuracy of 97.28% and 97.55% respectively, that surpasses other related methods on the same dataset. These findings highly encourage the reality of deep learning driven method for automatic and reliable detection of malaria while minimizing direct manual involvement.