Chest Cancer Classification

A Deep Learning project to classify the Chest images as normal or Carcinogenic with complete workflow from loading data to prediction and using MLflow to track experiments to record and compare parameters and results.

Getting Started

I have used the dataset from kaggle competition https://www.kaggle.com/datasets.

Broke the worklow into 4 Stages:

01_data_ingestion

02_prepare_base_model

03_model_trainer

04_model_evaluation

The complete set of packages I used are in the requirements.txt file in the root of the project.

The main endpoint of the project is the main.py file.

Tech Stack

Frameworks: Flask, tensorflow

Tools: MLflow, DVC, Dagshub

Installation

Install my-project with setting up the virtual environment using conda using the command conda activate your_env and then install all the dependencies in the requirements.txt file using the command pip install -r requirements.txt.

Environment Variables

To run this project, you will need to add the following environment variables to add in your virtual environment.

os.environ["MLFLOW_TRACKING_URI"] = "MLFLOW_TRACKING_URI"

os.environ["MLFLOW_TRACKING_USERNAME"] = "MLFLOW_USERNAME"

os.environ["MLFLOW_TRACKING_PASSWORD"] = "MLFLOW_PASSWORD"

Screenshots

Model info

Model Metrics

Usage/Examples

Configuring the data_ingestion_file

class ConfigurationManager:
    def __init__(
            self,
            config_filepath = CONFIG_FILE_PATH,
            params_filepath = PARAMS_FILE_PATH
    ):
        self.config = read_yaml(config_filepath)
        self.params = read_yaml(params_filepath)

        create_directories([self.config.artifacts_root])


    def get_data_ingestion_config(self) -> DataIngestionConfig:
        config = self.config.data_ingestion

        create_directories([config.root_dir])

        data_ingestion_config = DataIngestionConfig(
            root_dir=config.root_dir,
            source_URL=config.source_URL,
            local_data_file=config.local_data_file,
            unzip_dir=config.unzip_dir

        )

        return data_ingestion_config

Configuring the base_model_file

     

class ConfigurationManager:
    def __init__(
            self,
            config_filepath = CONFIG_FILE_PATH,
            params_filepath = PARAMS_FILE_PATH
    ):
        self.config = read_yaml(config_filepath)
        self.params = read_yaml(params_filepath)

        create_directories([self.config.artifacts_root])


    def get_prepare_base_model_config(self) -> PrepareBaseModelConfig:
        config = self.config.prepare_base_model

        create_directories([config.root_dir])

        prepare_base_model_config = PrepareBaseModelConfig(
            root_dir=Path(config.root_dir),
            base_model_path=Path(config.base_model_path),
            updated_base_model_path=Path(config.updated_base_model_path),
            params_image_size=self.params.IMAGE_SIZE,
            params_learning_rate=self.params.LEARNING_RATE,
            params_include_top=self.params.INCLUDE_TOP,
            params_weights=self.params.WEIGHTS,
            params_classes=self.params.CLASSES
           
        )

        return prepare_base_model_config 

Configuration for Model Training

class ConfigurationManager:
    def __init__(
            self,
            config_filepath = CONFIG_FILE_PATH,
            params_filepath = PARAMS_FILE_PATH
    ):
        self.config = read_yaml(config_filepath)
        self.params = read_yaml(params_filepath)

        create_directories([self.config.artifacts_root])


    def get_training_config(self) -> TrainingConfig:
        training = self.config.training
        prepare_base_model = self.config.prepare_base_model
        params = self.params
        training_data = os.path.join(self.config.data_ingestion.unzip_dir, "Data")
        create_directories([
            Path(training.root_dir)
        ])    

        training_config = TrainingConfig(
            root_dir=Path(training.root_dir),
            trained_model_path=Path(training.trained_model_path),
            updated_base_model_path=Path(prepare_base_model.updated_base_model_path),
            training_data=Path(training_data),
            params_epochs=params.EPOCHS,
            params_batch_size=params.BATCH_SIZE,
            params_is_augmentation=params.AUGMENTATION,
            params_image_size=params.IMAGE_SIZE
        )

        return training_config

Configuration of the Model Evaluation

class ConfigurationManager:
    def __init__(
            self,
            config_filepath = CONFIG_FILE_PATH,
            params_filepath = PARAMS_FILE_PATH):
        self.config = read_yaml(config_filepath)
        self.params = read_yaml(params_filepath)
        create_directories([self.config.artifacts_root])

    def get_evaluation_config(self) -> EvaluationConfig:
        eval_config = EvaluationConfig(
            path_of_model="artifacts/training/model.h5",
            training_data="artifacts//data_ingestion/Data",
            mlflow_uri="https://dagshub.com/RajatRathore123-github/Chest_Cancer_Classification.mlflow",
            all_params=self.params,
            params_image_size=self.params.IMAGE_SIZE,
            params_batch_size=self.params.BATCH_SIZE
        )    

        return eval_config

FAQ

Want to contribute to this project?

Create an issue with proper tag and make a PR regarding the same.

Something else?

Write to [email protected]

Authors

• @Rajat Rathore