#  Acute Myeloid & Lymphoblastic Leukemia Detection Model

## Acute Lymphoblastic Leukemia PyTorch Classifier 

 

# Table Of Contents

- [Introduction](#introduction)

- [DISCLAIMER](#disclaimer)

- [Getting Started](#getting-started)

- [ALL-IDB](#all-idb])

  - [ALL_IDB1](#all_idb1])

- [Contributing](#contributing)

  - [Contributors](#contributors)

- [Versioning](#versioning)

- [License](#license)

- [Bugs/Issues](#bugs-issues)

 

# Introduction

 PyTorch Acute Lymphoblastic Leukemia Detection using convolutional neural networks model.

 

# DISCLAIMER

This project should be used for research purposes only. Please don't use in hospital on real patiences

 

# Getting Started

To get started follow the [installation guide](Documentation/Installation.md) to find out how to clone the repository.

 

# ALL-IDB

You need to be granted access to use the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset. You can find the application form and information about getting access to the dataset on [this page](https://homes.di.unimi.it/scotti/all/#download) as well as information on how to contribute back to the project [here](https://homes.di.unimi.it/scotti/all/results.php). If you are not able to obtain a copy of the dataset please feel free to try this tutorial on your own dataset, we would be very happy to find additional AML & ALL datasets.

## ALL_IDB1 

In this project, [ALL-IDB1](https://homes.di.unimi.it/scotti/all/#datasets) is used, one of the datsets from the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset. We will use data augmentation to increase the amount of training and testing data we have.

"The ALL_IDB1 version 1.0 can be used both for testing segmentation capability of algorithms, as well as the classification systems and image preprocessing methods. This dataset is composed of 108 images collected during September, 2005. It contains about 39000 blood elements, where the lymphocytes has been labeled by expert oncologists. The images are taken with different magnifications of the microscope ranging from 300 to 500."

 

# Classifier

Model Results

| OS | Hardware | Training | Validation | Test | Accuracy | Recall | Precision | AUC/ROC |

| -------------------- | -------------------- | -------------------- | ----- | ---------- | ---------- | ---------- | ---------- | ---------- |

| Google Colab | Tesla K80 GPU | 1180 |  404 | 20 |  1.0 | 1.0 | 1.0 | 0.9948964 |

| Windows 10 | NVIDIA GeForce 940MX | 1180 |  404 | 20 |  1.0 | 1.0 | 1.0 | 0.9908836 |

 

# Contributing

Code contributions are welcome, bug fixes and enhancements from the Github.

Please read the [CONTRIBUTING](CONTRIBUTING.md "CONTRIBUTING") document for a full guide to forking our repositories and submitting your pull requests. You will also find information about our code of conduct on this page.

## Contributors

- Allen Akhaumere  - [Mascot Defense](https://mascotit.com.ng/ "Mascot Defense") Co-founder and CTO at Mascot Defense

 

# Versioning

 

# License

This project is licensed under the **MIT License** - see the [LICENSE](LICENSE.md "LICENSE") file for details.

 

# Bugs/Issues

I use the [repo issues](issues "repo issues") to track bugs and general requests related to using this project. See [CONTRIBUTING](CONTRIBUTING.md "CONTRIBUTING") for more info on how to submit bugs, feature requests and proposals.