This is a Oral cancer Detection Web-App, Build using Flask, Tensorflow, Keras and CV2. The Model is trained on preprocessed Histopathalogical Images of Oral Cancer with. Custom XceptionNet model is trained which detect the presence of cancer with 98% accuracy. Oral cancer is a serious public health issue worldwide, and early detection is crucial to improve survival rates and reduce the negative impact on patient's lives. While histopathological examination is the standard method for diagnosis, it can be time-consuming and prone to errors. However, recent advancements in deep learning, particularly convolutional neural neworks (CNNs), have shown promise in accurately diagnosing and analyzing biomedical images, making them a potential tool for early classification of cancer using histopathological image.
