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title

A COMPREHENSIVE SURVEY ON MACHINE LEARNING BASED AUTOMATED DETECTION OF BLOOD CANCER

Author(s):

Anuja Pawar

Keywords:

Blood Leukemia, Microscopic images, machine learning, automated classification, classification accuracy.

Abstract

Blood Leukemia is one of the most deadly diseases in the world with one of the most deadly mortality rates. The detection of blood leukemia at early stages is extremely difficult owing to the fact that leukemia’s symptoms do not manifest themselves completely early. Off late, artificial intelligence is being used in several applications of healthcare which are complex to be handled by conventional or traditional techniques. One such domain is the automated classification of leukemia using artificial intelligence based techniques. The study of previous work in the domain shows the fact that the classification accuracy is an extremely important parameter related to automated leukemia classification and attaining high accuracy is a difficult task. Several approaches have their pros and cons in this regard. This paper presents a comprehensive analysis of the various machine learning based approaches employed for automated blood leukemia detection, highlighting the salient features of each approach.

Other Details

Paper ID: IJSARTV
Published in: Volume : 8, Issue : 10
Publication Date: 10/2/2022

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