A comprehensive case study of deep learning on the detection of alpha thalassemia and beta thalassemia using public and private datasets

A comprehensive case study of deep learning on the detection of alpha thalassemia and beta thalassemia using public and private datasets

Abstract This study explores the performance of deep learning models, specifically Convolutional Neural Networks (CNN) and XGBoost, in predicting alpha and beta thalassemia using both public and private datasets. Thalassemia is a genetic disorder that impairs hemoglobin production, leading to anemia...

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Bibliographic Details
Main Authors: Muhammad Umar Nasir, Muhammad Tahir Naseem, Taher M. Ghazal, Muhammad Zubair, Oualid Ali, Sagheer Abbas, Munir Ahmad, Khan Muhammad Adnan
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
Subjects:
Thalassemia
Deep learning
CNN
XGBoost
Case study
Alpha thalassemia
Online Access:https://doi.org/10.1038/s41598-025-97353-0
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https://doi.org/10.1038/s41598-025-97353-0

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