Enhanced Early Detection of Oral Squamous Cell Carcinoma via Transfer Learning and Ensemble Deep Learning on Histopathological Images
Gurjot Kaur, Sheifali Gupta, Ashraf Osman Ibrahim, Salil Bharany, Marwa Anwar Ibrahim Elghazawy, Hadia Abdelgader Osman, Ali Husain Ahmed · International Journal of Advanced Computer Science and Applications · 2024
Oral Squamous Cell Carcinoma (OSCC) is one main kind of oral cancer; early diagnosis is rather important to increase patient survival chances. This study investigates the application of advanced deep learning techniques including transfer learning and ensemble learning to increase the accuracy of oral squamous cell cancer (OSCC) diagnosis using histopathological image analysis. Two transfer learning models, EfficientNetB3 and ResNet50, support the suggested method to extract suitable features from the histopathological images. Both models permit fine-tuning to improve their classification accuracy. On tests taken after the initial training, the EfficientNetB3 model scored 96.15%. Later on, training ResNet50 yielded a test accuracy of 91.40%. Weighted voting merged several models into an ensemble model designed to maximize the strengths of each network. With a test accuracy of 98.59% and a training accuracy of 99.34%, the ensemble model showed notably higher performance than the values obtained by the individual models. Divided into OSCC and standard categories, the collection has 5,192 extremely well-resolved images. The images were used to create training, validation, and testing sets. We used this method to consistently evaluate the model's performance and reduce overfitting. Furthermore, the ensemble model proved to be quite accurate with recall and F1 scoring, thereby proving its capacity to routinely identify OSCC images. Both groups produced ROC curves, and the area under the curve (AUC) demonstrated excellent model performance. Transfer learning and ensemble learning are used together in this study to show that OSCC can be found early and consistently in histopathology images. The findings reveal that the recommended strategy could be a consistent tool to assist pathologists in the precise and timely detection of OSCC, thereby improving patient treatment and outcomes.