Work

Comparative Analysis of Machine Learning Models for Disease Detection using Blood Cell Images

Year: 2024

Type: article

Abstract: This paper presents machine learning algorithms for blood test parameters estimation. The existing deep learning approaches for blood test parameters estimation are presented in this paper. The proposed machine learning algorithms for estimation of blood test parameters using blood cells images are described. The proposed deep learning algorithm for estimation of blood test parameters using blood cells images are described. The results and evaluations of the proposed model are presented. The machine learning algorithms decision trees, random forest, LightGBM, and SVM are proposed for blood test parameters estimation. While deep learning algorithm CNN is proposed for blood test parameters estimation in this research work.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.13729384

Authors: Kakade, Vikram, Bhagat, Amol P., Ali, M. S., Deshmukh, Sachin

Language: English

Topic: Digital Imaging for Blood Diseases

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W6930413016

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