OSCC Detector

• Tech Stack:
• Language: Python
• Libraries: Keras, TensorFlow, Scikit-Learn, Grad-CAM, LIME
• Notebook: Google Colab
• Github URL: Project Link

During our thesis work, our focus was on binary classification for the detection of Oral Squamous Cell Carcinoma in histopathological images. Drawing upon relevant research papers, we implemented state-of-the-art techniques to achieve compelling results. We used some CNN based models such as VGG-16, DenseNet-121, InceptionV3 and others. Each model exhibited distinct performance characteristics, yielding varying outcomes across different scenarios. To enhance the interpretability of our models and gain insights into their decision-making processes, we incorporated several explainable Artificial Intelligence (XAI) methods. Notably, we deployed techniques such as LIME, Score-CAM, and others. With the successful outcomes of our research, we are enthusiastic about the prospect of publishing a relevant paper. By sharing our methodologies and significant findings, we aim to contribute to the scientific community's knowledge base.