Adaptive Elastic GAN for High-Fidelity Blood Cell Image Hallucination and Classification

Adaptive Elastic GAN for High-Fidelity Blood Cell Image Hallucination and Classification

Automated blood cell classification is crucial for hematological analysis, yet the scarcity of annotated medical datasets challenges deep learning models. This study presents a novel semi-supervised Elastic Generative Adversarial Network that enhances classification accuracy, improves synthetic imag...

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Bibliographic Details
Main Authors: Issac Neha Margret, K. Rajakumar
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Blood cell classification
generative adversarial network (GAN)
image hallucination
medical image processing
peripheral blood smear cells
semi-supervised Wasserstein GAN (SS-WGAN)
Online Access:https://ieeexplore.ieee.org/document/10994417/
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