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

Authors

Institutions

Publication Details

Journal
Journal of Imaging
Published
2025-10-24
DOI
https://doi.org/10.3390/jimaging11110373
Primary Topic
Digital Imaging for Blood Diseases
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Raouf Dhaouadi, Amine Mosbah, Amina Bouatay, Aymen Yahyaoui et al.
Journal of Imaging
Digital Imaging for Blood Diseases
article

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

Raouf Dhaouadi, Amine Mosbah, Amina Bouatay, Aymen Yahyaoui, Oussama El Othmani
article en

Abstract

No abstract available for this paper.
Journal of ImagingVol. 11(11)
University of Monastir (TN), Tunisia Polytechnic School (TN), Hôpital Sahloul (TN), Military Hospital of Tunis (TN), Manouba University (TN), Universidad Politécnica de Madrid (ES)
Openalex Percentile: Top 70%
Digital Imaging for Blood Diseases
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.