Utilizing histopathological images to determine oral squamous cell carcinoma with deep convolutional neural networks

Abstract

One common kind of head and neck cancer that is typically identified at an advanced stage and has few treatment choices and a poor prognosis is oral squamous cell carcinoma (OSCC). Improving patient outcomes requires early detection. Histopathological examination of tissue samples, which provides in-depth understanding of cellular morphology and tissue architecture, continues to be the gold standard for the detection of cancer. Deep learning methods, in particular Convolutional Neural Networks (CNNs), have shown incredible potential in the last several years for a variety of medical imaging applications, such as histopathology analysis. In order to identify and categorize oral squamous cell carcinoma, this study investigates the use of deep CNNs in histopathological image analysis. We examine the present state of OSCC diagnosis, highlighting the drawbacks and limitations of conventional techniques. We then examine the design and operation of deep CNNs and discuss their prospects for image-guided cancer detection. We also go into the significance of duration of the dataset, preprocessing methods, and metrics unique to histopathology image assessment for the model. We offer experimental findings showing how CNN models perform on datasets of histopathology pictures of OSCC that are made accessible to the public. Our results indicate that the Xception model achieves the highest accuracy among individual models with 96.53%, while the Ensemble Model outperforms all with 98.73% accuracy. Other models like VGG16, MobileNetV2, and InceptionV3 also show high accuracy, with MobileNetV2 and InceptionV3 performing particularly well. Finally, we explore possible developments and future paths for using deep learning to diagnose OSCC, including multimodal data fusion, integration with clinical processes, and transfer learning.