Advances and Challenges in AI-Based Image Processing for Early Oral Cancer Detection: A Narrative Review

N. V. Soma Sekhar Vissamsetti; Gandla Shivakanth

doi:10.48084/etasr.17740

Authors

N. V. Soma Sekhar Vissamsetti Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, India
Gandla Shivakanth Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, India

Volume: 16 | Issue: 4 | Pages: 37996-38002 | August 2026 | https://doi.org/10.48084/etasr.17740

Received: 24 January 2026 | Revised: 26 February 2026 and 11 March 2026 | Accepted: 13 March 2026 | Online: 24 June 2026

Corresponding author: Gandla Shivakanth

Abstract

Oral squamous cell carcinoma (OSCC) is a major global health concern, particularly in low- and middle-income countries such as India, where tobacco and areca-nut use are prevalent. Early detection of oral potentially malignant disorders (OPMDs) greatly improves survival, yet conventional diagnostic methods, such as visual inspection and biopsy, are limited by subjectivity, invasiveness, and resource demands. The integration of artificial intelligence (AI) and digital image processing (DIP) has emerged as a promising solution for non-invasive, scalable screening. This narrative review synthesizes recent literature to provide a thematic overview of digital image processing and artificial intelligence approaches for oral cancer detection, rather than conducting a systematic or meta-analytic evaluation. This review analyzes recent research published on DIP techniques such as noise reduction, contrast enhancement, normalization, and segmentation, and their influence on AI-based classification across photographic, histopathological, and optical coherence tomography (OCT) images. Recent studies demonstrate that preprocessing consistency, hybrid feature extraction, and explainable AI improve diagnostic reliability and interpretability. However, most models remain constrained by small datasets, a lack of external validation, and limited feasibility for deployment. This paper identifies the need for standardized, end-to-end frameworks that integrate preprocessing, feature extraction, and classification to advance AI-based oral cancer detection from experimental feasibility to clinical reality.

Keywords:

oral squamous cell carcinoma, digital image processing, deep learning, machine learning, feature extraction, optical coherence tomography, explainable AI, early cancer detection, histopathology, clinical imaging

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