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A deep learning-based method for cervical transformation zone classification in colposcopy images.

Yuzhen Cao^1,2, Huizhan Ma^1,2, Yinuo Fan²

¹School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|September 12, 2022

Summary

A new deep learning method accurately classifies cervical transformation zones in colposcopy images. This AI tool aids in cervical cancer screening by improving classification accuracy.

Keywords:

Cervical cancer colposcopy deep learning feature fusion image processing

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Colposcopy is a key method for cervical cancer screening.
Cervical transformation zone typing is crucial for grading and treatment decisions.

Purpose of the Study:

To develop a deep learning (DL) method for automatic cervical transformation zone classification.
Utilize DL for enhanced accuracy in colposcopy image analysis.

Main Methods:

Proposed a multiscale feature fusion classification network.
Implemented cervical region detection followed by multiscale feature extraction and fusion.
Employed deep learning for automated classification.

Main Results:

Achieved the highest classification accuracy at 88.49% on the test dataset.
Demonstrated superior sensitivity compared to state-of-the-art models: Type 1 (90.12%), Type 2 (85.95%), Type 3 (89.45%).

Conclusions:

The developed DL method effectively automates cervical transformation zone classification from colposcopy images.
This AI tool can serve as a valuable auxiliary in cervical cancer screening protocols.