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Deep Learning Based Automatic Malaria Parasite Detection from Blood Smear and its Smartphone Based Application.

K M Faizullah Fuhad1, Jannat Ferdousey Tuba1, Md Rabiul Ali Sarker1

  • 1Department of Electrical & Computer Engineering, North South University, Dhaka 1229, Bangladesh.

Diagnostics (Basel, Switzerland)
|May 24, 2020
PubMed
Summary

This study introduces an automated deep learning model for malaria diagnosis using Convolutional Neural Networks (CNNs). The model achieves high accuracy (99.23%) in detecting Plasmodium parasites from blood smear images, reducing the need for expert microscopists.

Keywords:
AutoencoderCNNPlasmodium parasitesblood smeardata augmentationdeep learningfloating point operationsinference performanceknowledge distillationmicroscopic

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Area of Science:

  • Medical diagnostics
  • Artificial intelligence in healthcare
  • Parasitology

Background:

  • Malaria diagnosis relies on manual microscopy of blood smears by trained experts.
  • This manual process is time-consuming, resource-intensive, and prone to human error.
  • There is a critical need for automated, accurate, and efficient diagnostic tools to combat malaria.

Purpose of the Study:

  • To develop and validate an automated Convolutional Neural Network (CNN) based model for malaria parasite detection in microscopic blood smear images.
  • To optimize the model's accuracy and inference performance using techniques like knowledge distillation, data augmentation, and autoencoders.
  • To assess the practical deployability and efficiency of the developed model in real-world scenarios.

Main Methods:

  • An automated malaria diagnosis model was designed using Convolutional Neural Networks (CNNs).
  • Techniques such as knowledge distillation, data augmentation, and autoencoders were employed for model optimization.
  • Feature extraction was performed by a CNN, with classification using Support Vector Machines (SVM) or K-Nearest Neighbors (KNN) under three distinct training procedures.
  • The model was evaluated for accuracy and computational efficiency (floating point operations).

Main Results:

  • The developed deep learning model achieved a high diagnostic accuracy of 99.23% for detecting malarial parasites.
  • The model requires minimal computational resources, with just over 4600 floating point operations.
  • The miniaturized model demonstrated efficient performance, with inference times under 1 second per sample when deployed on mobile phones and a web application.

Conclusions:

  • The automated CNN-based model offers a highly accurate and efficient solution for malaria diagnosis from microscopic images.
  • The model's low computational requirements and fast inference times make it suitable for practical deployment in resource-limited settings.
  • This AI-driven approach has the potential to significantly reduce the reliance on manual microscopy, improving malaria detection accessibility and speed.