AI-Assisted Diagnosis of Oral Cancer: A Deep Learning Approach for Early Detection Using Histopathological Imaging

Abstract

The prevalence of OSCC (oral squamous cell carcinoma) continues to be a leading cause of death from cancer world-wide. Even though substantial developments have occurred in medicine, OSCC can often be diagnosed too late. In addition, due to the paucity of screening facilities are available, especially in under-developed areas, Through the utilization of invasive diagnostic procedures such as biopsies continue to be the primary means of diagnosing OSCC. As such, the need for developing non-invasive diagnostic tools continues to grow. In this paper, we present an artificial intelligence-based deep learning framework that uses histopathological images to detect and classify OSCC. As such, the imperative of developing non-invasive diagnostic tools continues to grow. This model is founded upon the principle of transfer learning from the pre-trained CNNs (Convolutional Neural Networks) MobileNet, VGG16, and ResNet5, and were trained on curated datasets of histopathological images of oral tissues classified into normal, pre-cancerous and cancerous. We also employed numerous image preprocessing techniques (resizing, normalization, rotation, flipping, and brightness adjustments) to augment the features of our dataset, which was necessary to strengthen the learning The diagnostic utility of the proposed model and to increase its feature robustness. To further improve the accuracy and reduce false negative results, we implemented a soft voting ensemble method. Soft voting is a weighted version of hard voting and combines the probabilistic output values of all of the separate individual CNN models. Commonly adopted performance metrics for classification models, namely accuracy,F1-score,recall, precision and ROC-AUC were implemented to assess the model’s overall performance. Additionally, Grad- CAM (Gradient-weighted Class Activation Mapping) was leveraged to visually demonstrate the most critical lesion regions with an intention to enhance the interpretability and clinical trustworthiness of the proposed model. The model was developed using both TensorFlow and PyTorch on a Google Colab GPU environment and presents a non-invasive, cost-effective, and explainable diagnostic tool to assist clinicians in performing early oral cancer screening and risk assessments by integrating AI-driven image analysis with clinical oncology. This work provides a foundation for future multimodal and transformer-based diagnostic systems, capable of transforming the early detection and handling of oral cancer cases.