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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Published on: April 14, 2023

Revolutionizing Oral Cancer Detection: An Approach Using Aquila and Gorilla Algorithms Optimized Transfer

Mahmoud Badawy^1,2, Hossam Magdy Balaha^2,3, Ahmed S Maklad^4,5

¹Department of Computer Science and Informatics, Applied College, Taibah University, Al Madinah Al Munawwarah 41461, Saudi Arabia.

Biomimetics (Basel, Switzerland)

|October 27, 2023

Summary

This summary is machine-generated.

Automated oral cancer detection using deep learning significantly improves early diagnosis. Optimized convolutional neural networks (CNNs) with novel metaheuristic algorithms achieved 99.25% accuracy, enhancing cost-effective screening.

Keywords:

Gorilla Troops Optimizer (GTO)classification convolutional neural network (CNN)deep learning (DL)

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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Pathology

Background:

Early oral cancer detection is crucial for patient survival but hindered by costly manual screenings.
Deep learning presents a cost-effective, automated solution for oral cancer screening using histopathology images.

Purpose of the Study:

To develop an accurate, automated framework for oral cancer classification from microscopic histopathology slides.
To evaluate the efficacy of optimized deep learning models in improving oral cancer detection accuracy.

Main Methods:

Utilized convolutional neural networks (CNNs) integrated with transfer learning (TL) models (VGG19, VGG16, MobileNet variants, NASNetMobile, DenseNet201).
Fine-tuned CNNs using the Aquila Optimizer (AO) and Gorilla Troops Optimizer (GTO) metaheuristic algorithms.
Trained and tested on the Histopathologic Oral Cancer Detection dataset (2494 normal, 2698 OSCC images).

Main Results:

The Aquila Optimizer (AO) generally outperformed the Gorilla Troops Optimizer (GTO) across most transfer learning models.
The DenseNet201 model achieved the highest accuracy, reaching 99.25% with AO and 97.27% with GTO.
The optimized deep learning framework demonstrated superior performance in distinguishing between normal and oral squamous cell carcinoma (OSCC) images.

Conclusions:

The proposed framework offers a significant advancement in automated oral cancer detection, enhancing diagnostic capabilities.
Optimized deep learning models, particularly CNNs with metaheuristic algorithms like AO, show immense potential for medical image analysis and diagnostics.
This approach addresses limitations in manual screening, paving the way for more accessible and accurate oral cancer diagnostics.