# Development of AI-Based Laryngeal Cancer Diagnostic Platform Using Laryngoscope Images

## TL;DR

This paper introduces an AI platform that accurately detects laryngeal cancer using laryngoscope images, combining vocal cord selection and lesion detection models.

## Contribution

The novel contribution is an AI-based diagnostic platform that integrates vocal cord selection with cancer localization for laryngeal cancer detection.

## Key findings

- The vocal cord selection model achieved a mean IoU of 0.6534 and a mean Dice score of 0.7692.
- The cancer detection model reached a mean IoU of 0.6469 and accuracy of 0.9860 with real-time inference.
- The platform enables accurate and fast detection of laryngeal cancer from laryngoscope images.

## Abstract

Objective: To develop and evaluate artificial intelligence (AI)-based models for detecting laryngeal cancer using laryngoscope images. Methods: Two deep learning models were designed. The first identified and selected vocal cord images from laryngoscope datasets; the second localized laryngeal cancer within the selected images. Both employed FCN–ResNet101. Datasets were annotated by otolaryngologists, preprocessed (cropping, normalization), and augmented (horizontal/vertical flip, grid distortion, color jitter). Performance was assessed using Intersection over Union (IoU), Dice score, accuracy, precision, recall, F1 score, and per-image inference time. Results: The vocal cord selection model achieved a mean IoU of 0.6534 and mean Dice score of 0.7692, with image-level accuracy of 0.9972. The laryngeal cancer model achieved a mean IoU of 0.6469 and mean Dice score of 0.7515, with accuracy of 0.9860. Real-time inference was observed (0.0244–0.0284 s/image). Conclusions: By integrating a vocal cord selection model with a lesion detection model, the proposed platform enables accurate and fast detection of laryngeal cancer from laryngoscope images under the current experimental setting.