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

Abstract

Image Quality Assessment (IQA) plays a critical role in image-based decision-making systems, especially in domains requiring high diagnostic precision. Effective feature information is a prerequisite for the high performance of machine learning methods in parasitic organism detection, and the quality of this feature information is influenced by the quality of the images. However, No-Reference IQA (NR-IQA) models have ignored microscopy-based datasets, particularly those involving parasitic organisms such as Cryptosporidium spp. and Giardia spp., which are vital for public health inspection. In this study, PRIQA (Parasite ResNet-101 IQA), a novel deep learning-based NR-IQA model specifically trained on a small parasite image dataset was presented. Using Mean Opinion Scores (MOS) from twenty human evaluators, nine Deep Convolutional Neural Network (DCNN) architectures were benchmarked and identified ResNet-101 as the most robust feature extractor. The features were mapped to MOS using regression models and compared with ten state-of-the-art NR-IQA algorithms. Experimental results demonstrated that PRIQA consistently outperforms existing methods, indicating its suitability as a practical quality control tool for identifying unreliable or low-quality parasite microscopy images and supporting more consistent downstream detection and diagnostic workflows in automated inspection systems.

Fig 1. Twenty-three reference images of Cryptosporidium spp. and Giardia spp. used for dataset generation.

Fig 2. Sample of a reference parasite image (Cryptosporidium spp. & Giardia spp.) with four different distortion images.

Fig 3. Sample of subjective evaluation.

Fig 4. General framework for CNN-based NR-IQA model selection.

Fig 5. Pipeline of the proposed Parasite ResNet-101 Image Quality Assessment (PRIQA) framework.

Fig 6. Scatter plots of Mean Opinion Score (MOS) against distortion levels for each distortion type.

(a) Gaussian White Noise (GWN), (b) Salt & Pepper Noise (SnP), (c) Speckle Noise, and (d) JPEG compression.These plots illustrate the relationship between increasing distortion intensity and subjective image quality as perceived by human evaluators.

Fig 7. Sensitivity analysis of RMSE performance for the top-performing models across different SVM hyperparameters.

(a–c) ResNet-101 with Cubic SVM for epsilon, box constraint, and kernel scale, respectively; (d–f) DarkNet-53 with Cubic SVM for epsilon, box constraint, and kernel scale, respectively; (g–i) EfficientNet-B0 with Quadratic SVM for epsilon, box constraint, and kernel scale, respectively.

Fig 8. Depth-wise linear CKA (clean vs. distorted) across nine backbones.

Bars denote Early, Mid, and Late layers for each model in four panels: (a) GWN, (b) Salt and Pepper (SnP), (c) Speckle, and (d) JPEG. Values ∈ [0,1]; higher = more similar.