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Updated: Jul 5, 2025

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing

Published on: December 1, 2023

Automated Laryngeal Cancer Detection and Classification Using Dwarf Mongoose Optimization Algorithm with Deep

Nuzaiha Mohamed¹, Reem Lafi Almutairi¹, Sayda Abdelrahim¹

¹Department of Public Health, College of Public Health and Health Informatics, University of Hail, Ha'il 81451, Saudi Arabia.

|January 11, 2024

Summary

This study introduces ALCAD-DMODL, an automated deep learning technique for laryngeal cancer detection. It improves accuracy and efficiency in identifying laryngeal cancer (LCA) from throat images.

Keywords:

Dwarf Mongoose Optimization deep learning endoscopy laryngeal cancer median filtering multi-head bidirectional gated recurrent unit

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Laryngeal cancer (LCA) incidence is rising globally, posing treatment challenges, especially in advanced stages.
Current LCA detection methods often lack accuracy, are computationally intensive, and require long screening times.
There is a need for efficient and accurate tools for early laryngeal cancer identification.

Purpose of the Study:

To develop an automated laryngeal cancer detection and classification technique using deep learning and optimization algorithms.
To enhance the accuracy and efficiency of laryngeal cancer diagnosis.
To address the limitations of existing LCA identification tools.

Main Methods:

The study presents the Automated Laryngeal Cancer Detection and Classification using a Dwarf Mongoose Optimization Algorithm with Deep Learning (ALCAD-DMODL) technique.
Median filtering (MF) was used for noise removal, followed by EfficientNet-B0 for feature extraction.
The Dwarf Mongoose Optimization (DMO) algorithm optimized EfficientNet-B0 hyperparameters, and a multi-head bidirectional gated recurrent unit (MBGRU) model performed classification.

Main Results:

The ALCAD-DMODL technique demonstrated superior performance in laryngeal cancer detection and classification.
Simulation results on a throat region image dataset confirmed the technique's effectiveness.
The method showed significant improvements over existing approaches in various performance metrics.

Conclusions:

The ALCAD-DMODL technique offers a promising automated solution for accurate and efficient laryngeal cancer diagnosis.
This deep learning approach, combined with optimization algorithms, can aid medical professionals in timely LCA identification.
The study highlights the potential of advanced AI methods in improving head and neck cancer management.