Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networks

TL;DR

This paper presents a deep convolutional neural network approach for detecting malaria parasites from red blood cell images, outperforming traditional methods with nearly 98% accuracy.

Contribution

The study introduces an end-to-end deep learning model for malaria detection from blood smear images, eliminating the need for manual feature engineering.

Findings

Achieved 97.77% accuracy on the NIH Malaria Dataset.

Demonstrated the effectiveness of deep CNNs over traditional feature extraction methods.

Validated model generalization with holdout testing.

Abstract

Malaria is a female anopheles mosquito-bite inflicted life-threatening disease which is considered endemic in many parts of the world. This article focuses on improving malaria detection from patches segmented from microscopic images of red blood cell smears by introducing a deep convolutional neural network. Compared to the traditional methods that use tedious hand engineering feature extraction, the proposed method uses deep learning in an end-to-end arrangement that performs both feature extraction and classification directly from the raw segmented patches of the red blood smears. The dataset used in this study was taken from National Institute of Health named NIH Malaria Dataset. The evaluation metric accuracy and loss along with 5-fold cross validation was used to compare and select the best performing architecture. To maximize the performance, existing standard pre-processing techniques from the literature has also been experimented. In addition, several other complex architectures have been implemented and tested to pick the best performing model. A holdout test has also been conducted to verify how well the proposed model generalizes on unseen data. Our best model achieves an accuracy of almost 97.77%.