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Erschienen in:
Comparative Analysis of Image Segmentation Techniques for Real Field Crop Images Kapitel lesen Erstes Kapitel lesen

2023 | OriginalPaper | Buchkapitel

Effectiveness of Machine Learning in Detecting Early-Stage Leukemia

verfasst von : Ketan Gupta, Nasmin Jiwani, Pawan Whig

Erschienen in: International Conference on Innovative Computing and Communications

Verlag: Springer Nature Singapore

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Abstract

The early identification and diagnosis of leukemia, i.e., the exact distinction of malignant leukocytes at the lowest possible cost in the early stages of the disease, is a key challenge in the domain of disease diagnostics. Considering the large frequency of leukemia, flow cytometry equipment is scarce, and the procedures available at laboratory diagnosis facilities are tedious, complex, and time-consuming. Inspired by the possibilities of machine learning (ML) in illness detection, the current critical search was done to examine the research attempting to find and categorize leukemia using ML algorithms. This research study provides a complete and systematic assessment of the current state of all available ML-based leukemia detection and classification algorithms that analyze PBS pictures. The accuracy rate of the ML techniques used in PBS image analysis to detect leukemia was greater than 93.5%, showing that the application of ML might lead to exceptional results in leukemia diagnosis from PBS pictures.

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Vorheriges Kapitel Assessing Permeability Prediction of BBB in the Central Nervous System Using ML
Nächstes Kapitel Convolutional Neural Networks (CNN) and DBSCAN Clustering for SARs-CoV Challenges: Complete Deep Learning Solution
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Metadaten
Titel
Effectiveness of Machine Learning in Detecting Early-Stage Leukemia
verfasst von
Ketan Gupta
Nasmin Jiwani
Pawan Whig
Copyright-Jahr
2023
Verlag
Springer Nature Singapore
Buch
International Conference on Innovative Computing and Communications

Print ISBN: 978-981-19-2534-4

Electronic ISBN: 978-981-19-2535-1

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-981-19-2535-1_34

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