Histopathologic Analysis of Oral Squamous Cell Carcinoma Biopsy with Deep Learning - Yadav, Aarti; Yadav, Surendra (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This study addresses the challenge of early Oral Squamous Cell Carcinoma (OSCC) detection via histopathologic analysis. Limited labelled datasets and complex image analysis hinder accurate models. The proposed solution involves transfer learning, utilizing a pre-trained Convolutional Neural Network (CNN) for the purpose of feature extraction from OSCC biopsy images. This aids in training a specialized prediction model. The dataset comprises OSCC biopsy images from Mahatma Gandhi Cancer Hospital & Research Institute, enhanced by data augmentation. Evaluation metrics include accuracy, sensitivity, specificity, and AUC-ROC. Preliminary findings showcase transfer learning’s potential, accurately predicting histopathologic oral cancer, even for unseen samples. This highlights the value of transfer learning in managing limited data scenarios, emphasizing improved OSCC diagnosis and patient care. The study concludes with a maximum 95% accuracy at a 3% training loss.