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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

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Deep-Learning-Based Automated Identification and Visualization of Oral Cancer in Optical Coherence Tomography Images.

Zihan Yang¹, Hongming Pan¹, Jianwei Shang²

¹Institute of Modern Optics, Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Nankai University, Tianjin 300350, China.

|March 29, 2023

Summary

This summary is machine-generated.

Deep learning algorithms applied to optical coherence tomography (OCT) images show promise for early oral cancer detection. These advanced methods significantly improve accuracy in identifying cancerous tissues compared to traditional machine learning approaches.

Keywords:

deep learning identification machine learning optical coherence tomography oral cancer

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Early detection of oral cancer is crucial for improved patient prognosis.
Current diagnostic technologies face challenges in accurate and automatic identification of oral lesions.
Optical coherence tomography (OCT) offers high resolution and non-invasive imaging for potential diagnostic aid.

Purpose of the Study:

To evaluate deep learning algorithms for analyzing OCT images to aid in oral cancer screening and diagnosis.
To compare the performance of deep learning models against traditional machine learning methods for oral tissue classification.

Main Methods:

An OCT dataset was created, comprising normal mucosa, precancerous lesions, and oral squamous cell carcinoma.
Three types of convolutional neural networks (CNNs) were trained and evaluated using accuracy, precision, sensitivity, and specificity.
CNN performance was benchmarked against machine learning approaches using the same dataset.

Main Results:

CNN-based methods achieved a classification accuracy of up to 96.76%.
Machine learning methods achieved an accuracy of 92.52%, indicating superior performance of CNNs.
Visualization techniques were employed to assess model interpretability in distinguishing oral tissues.

Conclusions:

Deep learning algorithms demonstrate significant potential for automated identification of oral cancer from OCT images.
These AI-driven tools can provide valuable decision support for clinicians in oral cancer screening and diagnosis.
The developed algorithms show promise for enhancing the accuracy and efficiency of oral cancer diagnostics.