A Unified Approach to Predict and Understand Acute Myeloid Leukemia Diagnosis
subject: Acute myeloid leukemia; Classification; Explainable AI; LIME; SHAP feature importance
description: Acute myeloid leukemia (AML) is a rapidly progressing disease that affects myeloid cells in blood and bone marrow. These abnormal cancerous cells called blast cells are non-functional cells that increase rapidly in bone marrow and are released into blood stream which crowd out the healthy functional cells leading to weak immune system. This life-threatening disease needs to be diagnosed at early stage and hence requires fully automated system for early detection of leukemia to aid pathologists and doctors. Most of the automated machine learning and AI models are not transparent and require techniques to explain model prediction. This paper presents methods to classify blood microscopic images into healthy or acute myeloid leukemia. Among all the methods implemented, Gradient Boosting outperforms with an accuracy of 96.67%. This paper also focuses on explainable AI to interpret model prediction and feature importance which further helps in understanding decision-making process of classification model and optimize it. 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Patil A.P.; Hiremath M.; Pawar V.
source: Lecture Notes in Electrical Engineering, Vol-1098, pp. 233-242.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 18761100; ISBN: 978-981997382-8
format: Online
language: English
type: Conference paper
coverage: Patil A.P., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Hiremath M., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Pawar V., Department of Computer Applications, CMR Institute of Technology, Bengaluru, India