Detecting Acute Lymphoblastic Leukemia using Tensorflow/oneAPI & Magic Leap Spatial Computing
The ALL Detection System for Magic Leap 1 combines 4 disruptive technologies to provide a proof of concept showing how Artificial Intelligence, the Internet of Things and Mixed Reality can be used together to change medical diagnostics.
The motivation for this project was the interest in combining Deep Learning with Magic Leap 1 to create a unique way of carrying out diagnostics tests using Computer Vision and Mixed Reality.
The project uses Magic Leap 1 & the Raspberry Pi 4 to provide a Spatial Computing detection system for classifying Acute Lymphoblastic Leukemia in unseen images of peripheral blood samples.
The Acute Lymphoblastic Leukemia oneAPI Classifer is used which hosts an API endpoint which exposes the classifier, allowing it process the images sent from the Magic Leap 1.
The dataset used to train the model and use the ALL Detection System for Magic Leap 1 is the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset by Fabio Scotti, Associate Professor at the University of Milan.
This project is released under the MIT License.
DISCLAIMER
This project should be used for research purposes only. The purpose of the project is to show the potential of Spatial Computing, Artificial Intelligence, and the Internet of Things for medical support systems such as diagnosis systems.
Although the classifier used in this project is very accurate and shows good results both on paper and in real world testing, it is not meant to be an alternative to professional medical diagnosis.
Developers that have contributed to this repository have experience in using Artificial Intelligence for detecting certain types of cancer & COVID-19. They are not a doctors, medical or cancer experts. Please use these systems responsibly.
