LUNGS-CANCER-DETECTION-AND-CLASSIFICATION

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LUNGS-CANCER-DETECTION-AND-CLASSIFICATION

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This project focuses on detecting lung cancer using Inception and Resnet50 models with the Chest CTScan dataset. Lung cancer is a leading cause of death worldwide, making early detection critical for successful treatment. Convolutional neural networks, such as Inception and Resnet50, have shown great success in image classification tasks.

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Data sets link: https://drive.google.com/file/d/1LSs5UCkC1hwgl8iD022pVrVowF-ACV4Q/view?usp=sharing 
# LUNGS-CANCER-DETECTION-AND-CLASSIFICATION
This project focuses on detecting lung cancer using Inception and Resnet50 models with the Chest CTScan dataset. Lung cancer is a leading cause of death worldwide, making early detection critical for successful treatment. Convolutional neural networks, such as Inception and Resnet50, have shown great success in image classification tasks.
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##Lungs Cancer Detection and Classification
This project is focused on detecting and classifying different types of lung cancers using medical images. The goal of this project is to help doctors and medical professionals in the early detection and diagnosis of lung cancer, which can greatly increase the chances of successful treatment and improved patient outcomes.

## Dataset
The dataset used in this project is a collection of medical images of lung cancer patients (CT Scans), obtained from various medical institutions. The dataset includes both benign and malignant lung tumors, as well as different subtypes of lung cancer. The dataset is preprocessed and cleaned to ensure accuracy and reliability of the results.

## Technologies Used
This project is built using Python and the following technologies:

TensorFlow and Keras for building and training the deep learning models
NumPy, Pandas, and Matplotlib for data processing and visualization
OpenCV for image processing and manipulation
Models Used
Two deep learning models are used for detecting and classifying lung cancer:

## Inception: 
This is a convolutional neural network (CNN) that uses an Inception architecture with multiple layers to extract high-level features from the images.
ResNet: This is another CNN that uses a residual network architecture, which allows for easier training of very deep networks.
Results.
The performance of the models is evaluated using metrics such as accuracy, precision, recall, and F1 score. The results show that both models achieve high accuracy in detecting and classifying lung cancer, with the Inception model slightly outperforming the ResNet model.

## Getting Started
To use this project, you will need to have Python 3 installed, along with the required libraries and dependencies listed in the requirements.txt file. You can clone the repository and run the main.py file to train and test the models on the dataset.
First of all, make sure that the terminal or IDE should be open in the project folder (lung cancer detection). Then, install all the required python packages which have been used in the code. Then run each cell (either all at once or individually). Make sure to have around 1GB of space in that drive (because the inception model will take up 1GB of space after saving).

## Conclusion
This project demonstrates the effectiveness of deep learning models in detecting and classifying lung cancer from medical images. With further development and optimization, these models can be used to assist medical professionals in the diagnosis and treatment of lung cancer, potentially leading to improved patient outcomes and survival rates.

LUNGS-CANCER-DETECTION-AND-CLASSIFICATION

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