A unified framework for oral squamous cell carcinoma detection using machine learning and deep learning techniques
Year: 2026
Type: article
Abstract: Oral Squamous Cell Carcinoma (OSCC) remains a significant global health burden due to its aggressive progression and poor prognosis when diagnosis is delayed. Although histopathological examination is the gold standard for OSCC diagnosis, it is time-intensive and highly dependent on specialist expertise, which can lead to diagnostic variability, especially in high-workload clinical settings. To address these challenges, this study proposes machine learning (ML) and deep learning (DL) based diagnostic models for automated classification of histopathological images to support early OSCC detection. The proposed framework integrates clinically relevant image preprocessing and feature enhancement strategies to improve diagnostic robustness. A Random Forest (RF)-based ML model achieves a test accuracy of 98.57%, while a custom Convolutional Neural Network (CNN) attains a test accuracy of 98.80%. Model performance is evaluated using accuracy, precision, F1-score, and AUC. The high diagnostic performance demonstrates the potential of the proposed models as clinical decision-support tools that can assist pathologists by reducing interpretation time, minimizing inter-observer variability, and improving diagnostic consistency. Such AI-driven systems may facilitate earlier diagnosis, timely treatment planning, and improved patient outcomes, highlighting their translational relevance for integration into routine histopathology workflows.
Source: Discover Computing
Authors: Ashutosh Mohapatra, Rasmita Dash
Institution: Siksha O Anusandhan University
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,520
OpenAlex ID: W7140111893