Work

Correction: El Othmani et al. AI-driven Automated Blood Cell Anomaly Detection: Enhancing Diagnostics and Telehealth in Hematology. J. Imaging 2025, 11, 157

Year: 2025

Type: erratum

Abstract: The authors wish to make the following corrections to the published paper [...].

Source: Journal of Imaging

DOI: 10.3390/jimaging11110373

Authors: Oussama El Othmani, Amine Mosbah, Aymen Yahyaoui, Amina Bouatay, Raouf Dhaouadi

Institutions: University of Carthage, Tunisia Polytechnic School, Military Academy, Tunisia, University of Monastir, Universidad Politécnica de Madrid, Hôpital Sahloul, Manouba University

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: gold

Estimated APC paid: $2,172

OpenAlex ID: W4415500646

Cites: 1

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