Acute Lymphoblastic Leukemia (ALL) Detection Using Deep Learning Models from PBS Images

Challenge Started!

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.

The problem

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 goals

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:

• Analyze the medical data images related to ALL.
• Detect the best features related to ALL PBS images.
• Investigate the best approaches to extracting the selected features in (2).
• Apply multiple machine learning and deep learning models over the extracted features to find the model with the highest accuracy for classifying the PBS images.
• Compare the results with previous works and analyze the output.
• Generate a recommendation report.