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<p>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.</p>

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A unified framework for oral squamous cell carcinoma detection using machine learning and deep learning techniques

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月23日

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  • Ashutosh Mohapatra,
  • Rasmita Dash

摘要

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.