Oral Cancer Detection

This project focuses on detecting histopathologic oral cancer using Convolutional Neural Networks (CNNs).

Features

• Trained a deep learning model using histopathology images
• Achieved high accuracy in classifying cancerous vs. non-cancerous images
• Visualized performance with loss and accuracy plots

Dataset

The dataset consists of 4946 histopathology images of oral cancer. It includes both positive (cancerous) and negative (non-cancerous) samples.

Model

• Architecture: Custom CNN with multiple convolutional and pooling layers
• Optimizer: AdamW
• Loss Function: Categorical Crossentropy
• Metrics: Accuracy

How to Run

1. Clone this repository
2. Install dependencies:

   pip install tensorflow matplotlib numpy seaborn cv2 

3. Run the notebook:

   jupyter notebook kan oraldataset.ipynb

Results

• Accuracy: 92.45%
• Model format: .keras

Authors

• Yash Karnani