Project Abstract

Analyzing Healthcare Data: Transforming All DiagnosisThe current diagnosis process for acute lymphoblastic leukemia is intrusive and slow, increasing the risk of misdiagnosis and postponing treatment. Through the analysis of peripheral blood smear (PBS) images, this project seeks to revolutionize the field of ALL diagnosis by utilizing contemporary computational techniques, including machine learning and computer vision.The goal of this research is to improve and accelerate the ALL-diagnosis process. The objective is to provide a fast and dependable way of quickly identifying all instances by automating the diagnosis process utilizing machine learning and image processing techniques, ultimately leading to better patient outcomes.Using machine learning for medical condition classification and detection, as well as creating deep learning models that can recognize complex symptoms, are some of the research’s main goals.

Keywords: Leukemia Diagnosis Automation, Machine Learning for all Diagnosis, Image Processing in Cancer Detection

 Conference Details

Session: Presentation Stream 19 at Presentation Slot 8

Location: GH018 at Wednesday 8th 09:00 – 12:30

Markers: Betsy Dayana Marcela Chaparro Rico, Jens Blanck

Course: MSc Advanced Computer Science, Masters PG

Future Plans: I’m looking for an industry placement