Using a Deep Learning CNN to detect acute lymphoblastic leukemia (ALL) from blood microscopy
Classifiers created with Tensorflow 2 and using Fabio Scotti's ALL-IDB (Acute Lymphoblastic Leukemia Image Database for Image Processing) dataset.
The ALL 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.
Source code for the 2021 ICASSP paper "Acute Lymphoblastic Leukemia detection based on adaptive unsharpening and Deep Learning"
Intel DevMesh AI Spotlight Award winner. Acute Lymphoblastic Leukemia Detection System 2019 uses Tensorflow 1.4.1 & Neural Compute Stick 1 to provide an intelligent network and diagnosis system. Project by Adam Milton-Barker.
A repository dedicated to sharing the AML/ALL related public information, papers, code and datasets that we come across through R&D.
An Acute Lymphoblastic Leukemia classifier developed for the NVIDIA Jetson Nano. Jetson AI Certification project by Adam Milton-Barker.
Acute Lymphoblastic Leukemia Detection System 2020 uses Tensorflow 2 & Oculus Rift to provide a virtual detection system. Project by Adam Milton-Barker.
Source code for the 2021 CIVEMSA paper "Histopathological transfer learning for Acute Lymphoblastic Leukemia detection"
Combines Magic Leap's Spacial Computing technologies with Intel's oneAPI, OpenVINO & Neural Compute Stick to provide real-time classification of Acute Lymphoblastic Leukemia Lymphoblasts in peripheral blood samples within a Mixed Reality environment.
Molecular Diagnosis (MD) of Acute Lymphoblastic Leukemia (ALL): An integrative ALL classification system based on RNA-seq.
A series of Acute Lymphoblastic Leukemia CNNs programmed in Python using FastAI. Project by team member Salvatore Raieli.
Blood cancer is an uprising issue and doing physical medical procedures is too sensitive and time-consuming to detect any blast cell. Manual testing includes blood tests, spinal fluid tests, bone marrow tests, imaging tests, etc. A solution to this is to use modern methods in health care that help to detect diseases faster and increase the cure …
Source code for the 2022 CIVEMSA paper "ALLNet: Acute Lymphoblastic Leukemia detection using lightweight convolutional networks"
A HIAS compatible Acute Lymphoblastic Leukemia classifier trained using Intel Distribution for Python and Intel Optimized Tensorflow. Uses OpenVINO to deploy the model to a Raspberry Pi and Neural Compute Stick 2 for inference on the edge.
A free information application for Magic Leap 1 providing basic information in Mixed Reality about Leukemia, Haemopoiesis, Acute Myeloid & Lymphoblastic Leukemia
Combines Oculus Rift's Virtual Reality technologies with Intel's oneAPI, OpenVINO & Neural Compute Stick to provide real-time classification of Acute Lymphoblastic Leukemia Lymphoblasts in peripheral blood samples within a Virtual Reality environment.
This project is an application designed for complete blood cell counting and automated detection of Acute Lymphoblastic Leukemia (ALL) cells. It works by identifying different types of white blood cells, allowing for the extraction of lymphocyte cells. These cells can then be classified as either normal or indicative of ALL
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