Lung-Cancer-Classification-Model

This repository contains the Jupyter Notebook for a machine learning model designed to classify lung cancer using CT scan images. The model distinguishes between four types of classifications: Adenocarcinoma, Large Cell Carcinoma, Squamous Cell Carcinoma, and Normal tissue.

Project Overview

This project demonstrates the application of deep learning techniques to medical image classification. The model is trained using a convolutional neural network (CNN) architecture with ResNet50 as the backbone, fine-tuned to identify cancerous cells from CT scan images.

Key Features:

• Image Preprocessing: Includes data augmentation to ensure the model generalizes well across varied image orientations.
• ResNet50 Backbone: Leveraged for its state-of-the-art performance in image classification tasks, with modifications to prevent overfitting.
• High Accuracy: Achieved 95.6% accuracy on the test dataset, making it a robust tool for supporting radiologists in lung cancer detection.

Data

The dataset for this project was sourced from Kaggle and includes 1,000 labeled lung CT scan images. The data was divided into:

• 70% for training
• 10% for validation
• 20% for testing

Data Augmentation:

Data augmentation techniques such as image flipping, rotation, and zooming were applied to increase the robustness of the model.

Model Architecture

The model utilizes the following structure:

• ResNet50 Backbone: We use only 20 of the 50 activation layers to prevent overfitting while maintaining strong feature extraction capabilities.
• Batch Normalization: Applied to stabilize and accelerate the training process.
• Dropout: Added to further reduce overfitting.
• Dense Layers: Three fully connected layers are used, with the final layer being a softmax output to predict the four classes.

The model architecture is optimized for classifying CT scan images with high accuracy and minimal loss.

Setup Instructions

1. Clone the repository:

   git clone https://github.com/Rupert-S/Lung-Cancer-Classification-Model
   

2. Install required dependencies:

   pip install -r requirements.txt
   

3. Download the dataset from Kaggle and place it in the appropriate directory.

4. Run the Jupyter Notebook to preprocess the data, train the model, and generate predictions.