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Updated: Jul 21, 2025

Author Spotlight: Unlocking the Mysteries of Oral Potential Malignancies

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Published on: August 11, 2023

OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection.

Ramya Mohan¹, Arunmozhi Rama¹, Ramalingam Karthik Raja¹

¹Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India.

|July 29, 2023

Summary

This summary is machine-generated.

This study introduces OralNet, a novel framework for early oral cancer detection using histopathology images. OralNet achieves over 99.5% accuracy, aiding timely diagnosis and treatment.

Keywords:

DenseNet201 OSCC OralNet VGG16 classification oral cancer

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Multi-photon Imaging of Tumor Cell Invasion in an Orthotopic Mouse Model of Oral Squamous Cell Carcinoma

Multi-photon Imaging of Tumor Cell Invasion in an Orthotopic Mouse Model of Oral Squamous Cell Carcinoma

Published on: July 25, 2011

Area of Science:

Oncology
Medical Imaging
Computational Pathology

Background:

Rising global cancer incidence necessitates improved diagnostic tools.
Oral cancer, a subset of head and neck cancers, requires effective screening for early detection.
Histopathology images are crucial for definitive oral cancer diagnosis.

Purpose of the Study:

To develop and validate an automated framework, OralNet, for oral cancer detection from histopathology images.
To enhance the accuracy and efficiency of oral cancer screening.
To assess the clinical utility of deep learning and traditional methods in oral cancer diagnosis.

Main Methods:

Image collection and preprocessing of whole slide biopsy images at 100× and 400× magnifications.
Feature extraction using a hybrid approach combining deep learning and handcrafted features.
Feature reduction via the artificial hummingbird algorithm (AHA) and subsequent concatenation.
Binary classification of healthy versus oral squamous cell carcinoma (OSCC) images using three-fold cross-validation.

Main Results:

OralNet demonstrated high performance on a dataset of 3000 images (1500 healthy, 1500 OSCC).
The framework achieved an exceptional oral cancer detection accuracy exceeding 99.5%.
Validation confirmed the robustness and reliability of the OralNet framework.

Conclusions:

OralNet offers a clinically significant advancement in the automated detection of oral cancer from histopathology slides.
The proposed framework shows potential for integration into routine clinical practice for improved oral cancer screening.
Accurate and early detection via OralNet can significantly impact patient outcomes and treatment strategies.