An adaptive multiscale local mesh ternary pattern technique with extensive pre-processing and Grey Wolf optimisation based classifiers for oral cancer image classification.

Varun Srivastava, Khushi Garg, Samarth Soni, Arun Balodi, Manoj Tolani, Vikash Singh

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Varun SrivastavaDepartment of Computer Science and Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, 201309, India.

Khushi GargDepartment of Computer Science and Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, 201309, India.

Samarth SoniDepartment of Computer Science and Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, 201309, India.

Arun BalodiDepartment of Electronics and Communication Engineering, Dayananda Sagar University, Karnataka, 562112, Bengaluru, India.

Manoj TolaniDepartment of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, 201309, India.

Vikash SinghManipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. vikash.nepal@manipal.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study introduces an enhanced texture-based algorithm for the classification of oral cancer images. The images are first extensively preprocessed to enhance the affected area with techniques like gamma correction, adaptive histogram equalization, and sharpening of images using a Laplacian filter. Then a feature descriptor is extracted using an Adaptive Multiscale Local Mesh ternary patterns, which uses an adaptive threshold, and a sliding window of multiple scales. A machine learning model is then used for classification, which is also optimised using Grey Wolf optimisation. The pedagogy yields an overall average accuracy of 98.29% and 99.89% on two publicly available datasets. Also, the model is compared to three state-of-the-art techniques for oral cancer detection and is found to give an average improvement of 9.21% and 20.67% on the respective datasets.

Indexed as

Image Processing, Computer-AssistedMouth NeoplasmsPattern Recognition, AutomatedAlgorithmsClassification AlgorithmsHumansMachine LearningBiomedical image processingDeep learningImage ProcessingMachine learningTexture-based feature extraction

Identifiers

PMID41957480

DOI10.1038/s41598-026-47931-7

PMCPMC13223254

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.