Lung-Cancer-Prediction-System-Using-Hybrid-CNN-Machine-Learning-Algorithms

Using a CT-Scan image to predict the lung cancer. Machine learning algorithms includes Random Forest, Logistic Regression and Support Vector Machine()

Models Used

1. Convolutional Neural Network(CNN) - Support Vector Machine(SVM)
2. CNN - Logistic Regression(LR)
3. CNN - Random Forest(RF)

File Explanation

1. The "Data_Increasing.ipynb" file is responsible for generating additional image through data augmentation.
2. The "Preprocessing_Segmentation.ipynb" file is involved in both image preprocessing and classification tasks.
3. The "Lung Cancer Prediction System.ipynb" file is the main file for the proposed system.
4. The "dataset" file stores the lung cancer images, both before and after preprocessing.
5. The "best_model" file stores the best model for each type of model used in the system.
6. The "preprocessing" file stores the images produced during the preprocessing steps.
7. The "result" file stores the images that will be displayed in the Lung Cancer Prediction System.

References

Dataset used in this project (Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) lung cancer datase)
https://www.kaggle.com/datasets/hamdallak/the-iqothnccd-lung-cancer-dataset