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Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

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An Effective Convolutional Neural Network for Classifying Red Blood Cells in Malaria Diseases.

Quan Quan¹, Jianxin Wang¹, Liangliang Liu^2,3

¹School of Computer Science and Engineering, Central South University, Changsha, 410083, People's Republic of China.

Interdisciplinary Sciences, Computational Life Sciences

|May 13, 2020

Summary

This study introduces the Attentive Dense Circular Net (ADCN), a novel deep learning model for malaria diagnosis. ADCN significantly improves accuracy and reliability in classifying red blood cells (RBCs), outperforming existing methods.

Keywords:

Classification Convolutional neural networks Malaria Red blood cell

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Area of Science:

Medical Diagnostics
Artificial Intelligence in Healthcare
Parasitology

Background:

Malaria remains a significant cause of mortality globally.
Accurate and rapid malaria diagnosis is crucial for effective patient treatment.
Traditional diagnostic methods face limitations due to data scarcity and human factors, impacting prediction performance.

Purpose of the Study:

To propose an efficient and novel classification network, the Attentive Dense Circular Net (ADCN), for malaria diagnosis.
To leverage Convolutional Neural Networks (CNNs) combined with attention mechanisms for improved red blood cell (RBC) classification.
To enhance the reliability and performance of malaria detection systems.

Main Methods:

Developed the Attentive Dense Circular Net (ADCN), a CNN-based model inspired by residual and dense networks.
Integrated an attention mechanism into the ADCN architecture.
Trained and evaluated the ADCN model on a publicly available red blood cell (RBC) dataset.
Compared ADCN performance against established CNN models.

Main Results:

The proposed ADCN model demonstrated superior performance across all evaluated criteria.
ADCN achieved an accuracy of 97.47%, compared to 94.61% for the best-performing comparative model.
Sensitivity and specificity also showed significant improvements: 97.86% vs 95.20% (sensitivity) and 97.07% vs 92.87% (specificity).

Conclusions:

The Attentive Dense Circular Net (ADCN) offers a highly accurate and reliable approach for malaria diagnosis through RBC classification.
The novel architecture effectively addresses limitations of traditional methods and existing CNN models.
Further analysis is needed to understand the complexities and challenges in RBC classification.