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This Omdena Local Chapter Challenge runs for 4 weeks and is a unique experience to try and grow your skills in a collaborative and safe environment with a diverse mix of people from all over the world.
You will work on solving a local problem, initiated by the Omdena Irbid, Jordan Chapter.
Acute leukemia is a life-threatening disease common in children and adults that can lead to death if left untreated. Acute lymphoblastic leukemia (ALL) spreads out in children’s bodies rapidly and takes their lives within a few weeks [2].
The definitive identification of acute lymphoblastic leukemia (ALL) needs, invasive, costly, and time-consuming diagnostic procedures. A critical step in the early separation of cancer cases from non-cancer cases is ALL diagnosis using peripheral blood smear (PBS) images [5]. The manual diagnosing method is completely reliant on professionally trained medical specialists and their experience. And this makes the examination fraught with problems, such as diagnostic mistakes. Many other factors contribute to misdiagnosis, such as poor peripheral blood smear (PBS) machine quality images or the non-specific nature of ALL signs and symptoms. This project investigates the opportunity to explore the best machine learning techniques to improve the ALL diagnosis by analyzing the best features of the disease.
The project will explore the computer vision image filters until reaching the best filtered PBS image to extract the features. Multiple machine learning and deep learning models will be tested over the extracted features, and the best accuracy result will be adopted.
With a duration of 4-weeks, this project aims to:
Beginner-friendly, but also welcomes experts
Education-focused
Open-source
Duration: 4 to 8 weeks
The platform provides a great opportunity to work with industry experts and other AI professionals on a variety of innovative projects.
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