https://github.com/samir-atra/cancerdetector

The final project of AI50xIraq course is found in the commit on 21-3-2023
https://github.com/samir-atra/cancerdetector

cancer-detection cnn dataset inceptionv3 matplotlib numpy opencv tensorboard tensorflow tensorflow-tutorials transfer-learning

Last synced: 4 months ago
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The final project of AI50xIraq course is found in the commit on 21-3-2023

• Host: GitHub
• URL: https://github.com/samir-atra/cancerdetector
• Owner: Samir-atra
• License: cc0-1.0
• Created: 2022-06-11T09:30:13.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2023-12-27T19:24:57.000Z (over 2 years ago)
• Last Synced: 2025-04-07T16:19:41.924Z (about 1 year ago)
• Topics: cancer-detection, cnn, dataset, inceptionv3, matplotlib, numpy, opencv, tensorboard, tensorflow, tensorflow-tutorials, transfer-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 608 KB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# CancerDetector

This project is focused on the detection of brain tumors from MRI scans using deep learning. It originated as a final project and has since been updated and expanded. The project explores two different models for cancer detection: one for detecting meningioma tumors and another for classifying multiple types of brain tumors.

## Features

*   **Meningioma Tumor Detection:** A model to classify MRI scans as either positive or negative for meningioma tumors, achieving an accuracy of 93%.

*   **Multi-Class Tumor Detection:** A more advanced model that detects and classifies three types of brain tumors (Glioma, Meningioma, and Pituitary), achieving an accuracy of 95%.

*   **Transfer Learning:** Utilizes the InceptionV3 architecture with weights pre-trained on ImageNet for the multi-class detector, demonstrating the effectiveness of transfer learning for medical imaging tasks.

*   **Jupyter Notebooks:** The repository includes the Jupyter notebooks used for training, evaluation, and prediction, providing a clear view of the entire workflow.

## Models

This project includes two distinct models for brain tumor detection.

This project is focused on the detection of brain tumors from MRI scans using deep learning. It originated as a final project and has since been updated and expanded. The project explores two different models for cancer detection: one for detecting meningioma tumors and another for classifying multiple types of brain tumors.

## Features

*   **Meningioma Tumor Detection:** A model to classify MRI scans as either positive or negative for meningioma tumors, achieving an accuracy of 93%.

*   **Multi-Class Tumor Detection:** A more advanced model that detects and classifies three types of brain tumors (Glioma, Meningioma, and Pituitary), achieving an accuracy of 95%.

*   **Transfer Learning:** Utilizes the InceptionV3 architecture with weights pre-trained on ImageNet for the multi-class detector, demonstrating the effectiveness of transfer learning for medical imaging tasks.

*   **Jupyter Notebooks:** The repository includes the Jupyter notebooks used for training, evaluation, and prediction, providing a clear view of the entire workflow.

## Models

This project includes two distinct models for brain tumor detection.

### Meningioma Detector

This model is a Convolutional Neural Network (CNN) built from scratch using TensorFlow. It is designed for the binary classification of meningioma tumors.

*   **Architecture:**

    *   The model consists of three convolutional layers with 16, 32, and 64 filters, respectively.

    *   The `ELU` (Exponential Linear Unit) activation function is used in all convolutional and dense layers.

    *   `MaxPooling2D` is applied after each convolutional layer to downsample the feature maps.

    *   Two fully-connected (`Dense`) layers with 128 units each follow the convolutional layers.

    *   The final output layer uses a `softmax` activation function for classification.

*   **Regularization:** To prevent overfitting, the model employs two regularization techniques:

    *   `Dropout` with a rate of 0.2 is applied after each pooling layer and between the dense layers.

    *   `L2 regularization` is applied to the weights of all convolutional and dense layers.

*   **Performance:** This model achieves an accuracy of 93% on the test set.

### Multi-Class Cancer Detector

This model is designed to classify MRI scans into four categories: Glioma, Meningioma, Pituitary tumor, or no tumor. It leverages transfer learning to achieve high accuracy.

*   **Technique:** Transfer learning and fine-tuning.

*   **Base Model:** The `InceptionV3` model, pre-trained on the ImageNet dataset, is used as the base for feature extraction. The original classification head of InceptionV3 is removed.

*   **Custom Head:** A new classification head is added on top of the InceptionV3 base, which includes:

    *   A `GlobalAveragePooling2D` layer.

    *   A `Dense` output layer with 4 units and a `softmax` activation function.

*   **Training Process:**

    1.  **Feature Extraction:** The model is first trained with the InceptionV3 base frozen (weights are not updated). This allows the new classification head to adapt to the brain tumor dataset.

    2.  **Fine-Tuning:** After the initial training, the InceptionV3 base is unfrozen, and the entire model is trained with a very low learning rate. This fine-tunes the pre-trained weights to be more specific to the task of tumor detection. During this phase, the `BatchNormalization` layers in InceptionV3 are kept frozen to stabilize training.

*   **Data Augmentation:** To improve generalization, the training data is augmented with `RandomFlip` (horizontal) and `RandomRotation`.

*   **Performance:** This model achieves an accuracy of 95% on the test set.

## Datasets

The models were trained on datasets from Kaggle:

*   **Brain Tumor Classification (MRI):** [https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri](https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri)

*   **Brain Tumor MRI Dataset:** [https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset)

## Installation and Usage

To use this project, you will need to have Python, TensorFlow, and other standard data science libraries installed. The `Transfer.ipynb` and `Train&Test.ipynb` notebooks in the `Cancer_Detector` and `Meningioma_Detector` directories, respectively, contain the code for training the models.

The pre-trained model file for the multi-class detector can be found at the following link:

[https://drive.google.com/drive/folders/1o7ts623pJQxxuOs5kQBkyjEyorH8lT0X?usp=sharing](https://drive.google.com/drive/folders/1o7ts623pJQxxuOs5kQBkyjEyorH8lT0X?usp=sharing)