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[](https://www.python.org/dev/peps/pep-0008/)[](https://all-arduino-nano-33-ble-sense-classifier.readthedocs.io/en/latest/?badge=latest)[](https://bestpractices.coreinfrastructure.org/projects/5065)
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# Introduction
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The **Acute Lypmhoblastic Leukemia Arduino Nano 33 BLE Sense Classifier** is an experiment to explore how low powered microcontrollers, specifically the Arduino Nano 33 BLE Sense, can be used to detect Acute Lymphoblastic Leukemia. The Arduino Nano 33 BLE Sense is the latest Arduino Board which supports Tensorflow Lite, allowing machine learning on Arduino.
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The **Acute Lypmhoblastic Leukemia Arduino Nano 33 BLE Sense Classifier** is an experiment to explore how low powered microcontrollers, specifically the Arduino Nano 33 BLE Sense, can be used to detect Acute Lymphoblastic Leukemia. The [Arduino Nano 33 BLE Sense](https://store.arduino.cc/arduino-nano-33-ble-sense) is the latest Arduino Board which supports Tensorflow Lite, allowing machine learning on Arduino.
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The model you will train is a 6 layer Convoluntional Neural Network trained using [Intel® Optimization for Tensorflow*](https://software.intel.com/content/www/us/en/develop/articles/intel-optimization-for-tensorflow-installation-guide.html) from the [Intel® oneAPI AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit/download.html?operatingsystem=linux) to optimize and accelerate the training process.
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Checkout the [official video](https://www.youtube.com/watch?v=CDJEXdj2KZs) for the project.
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# Acute Lymphoblastic Leukemia
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[Acute lymphoblastic leukemia (ALL)](https://www.leukemiaairesearch.com/research/leukemia), also known as Acute Lymphocytic Leukemia, is a cancer that affects the lymphoid blood cell lineage. It is the most common leukemia in children, and it accounts for 10-20% of acute leukemias in adults. The prognosis for both adult and especially childhood ALL has improved substantially since the 1970s. The 5- year survival is approximately 95% in children. In adults, the 5-year survival varies between 25% and 75%, with more favorable results in younger than in older patients.
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[Acute lymphoblastic leukemia (ALL)](https://www.leukemiaairesearch.com/research/leukemia), also known as acute lymphocytic leukemia, is a cancer that affects the lymphoid blood cell lineage. It is the most common leukemia in children, and it accounts for 10-20% of acute leukemias in adults. The prognosis for both adult and especially childhood ALL has improved substantially since the 1970s. The 5- year survival is approximately 95% in children. In adults, the 5-year survival varies between 25% and 75%, with more favorable results in younger than in older patients.
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For more information about Acute Lymphoblastic Leukemia please visit our [Leukemia Information Page](https://www.leukemiaairesearch.com/research/leukemia)
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# ALL-IDB
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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.
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# Welcome
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Welcome to the **ALL Arduino Nano 33 BLE Sense Classifier** official documentation.
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The **Acute Lypmhoblastic Leukemia Arduino Nano 33 BLE Sense Classifier** is an experiment to explore how low powered microcontrollers, specifically the Arduino Nano 33 BLE Sense, can be used to detect Acute Lymphoblastic Leukemia. The Arduino Nano 33 BLE Sense is the latest Arduino Board which supports Tensorflow Lite, allowing machine learning on Arduino.
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The motivation for this project was to explore how low powered devices such as Arduino can be used to detect Acute Lymphoblastic Leukemia. The project will be submitted to the Tensorflow For Microcontroller Challenge and the Eyes on Edge: tinyML Vision Challenge.
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Welcome to the [ALL Arduino Nano 33 BLE Sense Classifier](https://github.com/AMLResearchProject/ALL-Arduino-Nano-33-BLE-Sense-Classifier) official documentation.
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# Installation
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Use the following installation guides to set up your project:
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Use the following installation guides to set up your project.:
-[Getting started with the Arduino Nano 33 BLE Sense](https://www.arduino.cc/en/Guide/NANO33BLESense)
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-[Why doesn't the 5V pin work in the Arduino Nano 33 BLE boards?](https://support.arduino.cc/hc/en-us/articles/360014779679-Why-doesn-t-the-5V-pin-work-in-the-Arduino-Nano-33-BLE-boards-)
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Follow the diagram above to connect your SD card reader to the Arduino Nano 33 BLE Sense.
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Follow the diagram above to connect your SD card reader to the Arduino Nano 33 BLE Sense. Remember you need to follow the steps in [Why doesn't the 5V pin work in the Arduino Nano 33 BLE boards?](https://support.arduino.cc/hc/en-us/articles/360014779679-Why-doesn-t-the-5V-pin-work-in-the-Arduino-Nano-33-BLE-boards-) to enable 5V on the Arduino Nano BLE Sense.
This will clone the ALL Arduino Nano 33 BLE Sense Classifier repository and move the cloned repository to the agents directory in the HIAS project (components/agents/mqtt/).
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This will clone the ALL Arduino Nano 33 BLE Sense Classifier repository.
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```bash
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Developers from the Github community that would like to contribute to the development of this project should first create a fork, and clone that repository. For detailed information please view the [CONTRIBUTING](https://github.com/AMLResearchProject/ALL-Arduino-Nano-33-BLE-Sense-Classifier/blob/master/CONTRIBUTING.md"CONTRIBUTING") guide. You should pull the latest code from the development branch.
The **-b "1.0.0"** parameter ensures you get the code from the latest master branch. Before using the below command please check our latest master branch in the button at the top of the project README.
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The **-b "2.0.0"** parameter ensures you get the code from the latest master branch. Before using the below command please check our latest master branch in the button at the top of the project README.
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# Test Data
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During training the test data was resized and moved to the **model/data/test/** directory. Before you can continue you need to upload these files to the SD card.
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# Run The Classifier
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Now it is time to run your classifier on the Arduino Nano 33 BLE Sense. Make sure you are connected to your Arduino and click on the **upload** button. Once the model is uploaded it will start to run, open your serial monitor and watch the output.
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Now it is time to run your classifier on the Arduino Nano 33 BLE Sense. Make sure you are connected to your Arduino and click on the **upload** button. Once the model is uploaded it will start to run, open your serial monitor and watch the output. You will see the onboard LED on the Arduino Nano 33 BLE Sense turn **red** if Acute Lymphoblastic Leukemia is detected and **green** if it is not.
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```bash
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# Conclusion
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We see that our model that can correctly classify all twenty images only gets 11/20 when running on Arduino. There are some additional testing steps we can take which will be introduced in V2 that will allow us to test the Arduino model on our development machine to help identify where the bug is coming from. For now this is a good first attempt at building a classifier to detect Acute Lymphoblastic Leukemia detection on Arduino. If you would like to view the ongoing issue in the Tensorflow Micro repository [click here](https://github.com/tensorflow/tflite-micro/issues/287), thanks to [Advait Jain](https://github.com/advaitjain) for the asistance with this issue.
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We see that our model that can correctly classify all twenty images only gets 11/20 when running on Arduino. There are some additional testing steps we can take which will be introduced in V2 that will allow us to test the Arduino model on our development machine to help identify where the bug is coming from. For now this is a good first attempt at building a classifier to detect Acute Lymphoblastic Leukemia detection on Arduino. If you would like to view the ongoing issue in the Tensorflow Micro repository [click here](https://github.com/tensorflow/tflite-micro/issues/287) thanks to [Advait Jain](https://github.com/advaitjain) for the asistance with this issue.
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