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Updated: Oct 31, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

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Single-cell conventional pap smear image classification using pre-trained deep neural network architectures.

Mohammed Aliy Mohammed¹, Fetulhak Abdurahman², Yodit Abebe Ayalew³

¹School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia. mohaliyet@gmail.com.

BMC Biomedical Engineering

|June 30, 2021

Summary

This summary is machine-generated.

Deep neural networks (DNNs) show promise for automating cervical cancer screening. DenseNet169 achieved high accuracy in classifying single-cell Pap smear images, outperforming benchmarks.

Keywords:

CNN Cervical cancer Deep learning Image classification Pap smear

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Area of Science:

Medical Imaging
Computational Pathology
Artificial Intelligence in Healthcare

Background:

Automating cervical cancer screening via cytology can address pathologist shortages in developing nations.
Deep neural networks (DNNs) offer high accuracy and reproducibility for image classification tasks.
Previous automated approaches include semi- and fully automated methods using computer vision.

Purpose of the Study:

To classify single-cell Pap smear images using pre-trained deep convolutional neural network (DCNN) image classifiers.
To fine-tune and evaluate top-performing DCNNs on the SIPaKMeD dataset for cervical cytology.

Main Methods:

Fine-tuned ten pre-trained DCNNs from Keras Applications, selected for top 1% accuracy.
Evaluated DCNN performance on five-class single-cell Pap smear images from the SIPaKMeD dataset.
Utilized metrics including accuracy, precision, recall, and F1-score for performance assessment.

Main Results:

DenseNet169 achieved the highest performance among the evaluated DCNNs.
DenseNet169 demonstrated superior average accuracy (0.990), precision (0.974), recall (0.974), and F1-score (0.974).
The model surpassed the dataset's benchmark accuracy by 3.70%.

Conclusions:

DenseNet169, despite its smaller size, is not optimized for mobile or edge devices.
Further research is needed on smaller DCNNs for real-world mobile/edge deployment.
Validation on diverse datasets is crucial to enhance model generalizability beyond the SIPaKMeD dataset.