Counting and Classification of Malarial Parasite From Giemsa-Stained Thin Film Images

Abstract

Malaria is a life-threatening disease causing by an infection of the protozoan parasite Plasmodium. Plasmodium falciparum is the deadliest and most common human infected parasites hosted by anopheles mosquito vector. To cure a malaria infected patient and prevent further spreading, malaria diagnosis using microscopy to visualize Giemsa-stained parasites is commonly done. The microscopy diagnosis is somewhat time consuming and requires well-trained malaria experts to interpret what they see under the microscope. To address this limitation, an automated malaria infected diagnosis is needed. This work proposed a computer-aided automated diagnosis system that can perform remote field diagnosis with high accuracy while requiring less computational demands. The proposed framework consists of two main parts that are red blood cell counting and parasite life-cycle stage classification. The counting process is performed by computer vision techniques, namely Hough transform. Different machine learning techniques, i.e., Multilayer Perceptron, Linear Discriminant Analysis, Support Vector Machine, and Weighted Similarity Extreme Learning Machine, are employed in the classification task. We also demonstrated that combining hand-crafted and deep-learned features can enhance the overall performance of the framework. The experimental results showed that the proposed methods could correctly count and classify at 97.94% and 98.12% accuracy, respectively. The overall proposal system can achieve at 96.18% accuracy. This is achieved by WELM in conjunction with deep-learned (AlexNet_FC7) and the hand-crafted (color) features. INDEX TERMS Combining features, Giemsa-stained thin film, malaria.

SELM: Siamese extreme learning machine with application to face biometrics

“…WELM can reduce training time because it does not need any tuning of the kernel parameters. It yields better performance, especially when dealing with similarity-based tasks [ 32 , 41 ]. In WELM, the matrix in conventional activation is replaced by: The set of weights are randomly selected from a training set , thus, .…”

An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis

“…Rahman et al [30] also exploited TL strategies using both natural and medical images and performed an extensive test of some off-the-shelf CNNs to realise a binary classification. Some other techniques not explored in this work are based on the combination of CNNextracted features and handcrafted ones [31][32][33] or the direct use of object detectors [34]. For example, Kudisthalert et al [33] proposed a malaria parasite detection system, based on the combination of handcrafted and deep features, extracted from pretrained AlexNet.…”

“…Some other techniques not explored in this work are based on the combination of CNN-extracted features and handcrafted ones [ 31 , 32 , 33 ] or the direct use of object detectors [ 34 ]. For example, Kudisthalert et al [ 33 ] proposed a malaria parasite detection system, based on the combination of handcrafted and deep features, extracted from pretrained AlexNet. Abdurahman et al [ 34 ] realised a modified version of the YOLOV4 detector.…”

SELM: Siamese Extreme Learning Machine with Application to Face Biometrics

“…WELM can reduce training time because it does not need any tuning of the kernel parameters. It yields better performance especially when dealing with similarity-based tasks [32,41]. In WELM, the H matrix in conventional activation is replaced by:…”