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Deep learning-based no-reference image quality assessment framework for Cryptosporidium spp. and Giardia spp.

Muhammad Amirul Aiman Asri1, Heshalini Rajagopal2, Norrima Mokhtar1

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A new deep learning model, PRIQA, assesses parasite image quality without references. It outperforms existing methods, ensuring reliable microscopic analysis for public health.

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Area of Science:

  • Parasitology
  • Medical Imaging
  • Computer Science

Background:

  • Image Quality Assessment (IQA) is crucial for diagnostic accuracy.
  • No-Reference IQA (NR-IQA) models lack focus on microscopy datasets, especially for parasites like Cryptosporidium and Giardia.
  • High-quality features are essential for machine learning in parasitic organism detection.

Purpose of the Study:

  • To develop a novel deep learning-based NR-IQA model for parasite microscopy images.
  • To address the gap in NR-IQA for microscopic parasitic organism datasets.
  • To improve the reliability of automated inspection systems for public health.

Main Methods:

  • Developed PRIQA (Parasite ResNet-101 IQA), a deep learning NR-IQA model.
  • Benchmarked nine Deep Convolutional Neural Network (DCNN) architectures using human Mean Opinion Scores (MOS).
  • Used ResNet-101 as the feature extractor, mapping features to MOS via regression, and compared with ten state-of-the-art NR-IQA algorithms.

Main Results:

  • ResNet-101 was identified as the most robust feature extractor for parasite images.
  • PRIQA demonstrated superior performance compared to existing NR-IQA methods.
  • The model effectively identifies unreliable or low-quality parasite microscopy images.

Conclusions:

  • PRIQA is a suitable tool for practical quality control in parasite image analysis.
  • The model enhances consistency in downstream detection and diagnostic workflows.
  • This work supports more accurate public health inspection through improved image quality assessment.