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.
