Peer-reviewedCC BYDiagnostics (Basel, Switzerland)
Machine Learning and Deep Learning for Early Disease Detection Across Modalities: A Comprehensive Review of Methods, Applications, and Clinical Translation
- 1Electrical and Electronics Engineering Department, , 78050 Karabük, Türkiye
- 2Department of Electrical and Electronics Engineering, Faculty of Engineering, , 27310 Gaziantep, Türkiye;
- 3Department of Drone Techniques Engineering, Technical College of Mosul, , Mosul 41001, Iraq;
Related
- Machine Learning-Based Fatigue Detection from EEG Signals: A Methodological Pilot Framework with Potential for Nursing Applications
- Artificial Intelligence for Colorectal Cancer Diagnosis: A Comprehensive Survey of Modalities, Frameworks, and Clinical Tasks
- Analyzing diagnostic modalities for malaria detection and surveillance in varied belts: Burning need
- Novel strategies against drug-resistant tuberculosis: a comprehensive review from molecular mechanisms to clinical translation
- Pulsed Low-Dose-Rate Radiotherapy (PLDR): A Comprehensive Review of Radiobiological Rationale and Clinical Applications in Modern Cancer Care
BibTeX
@article{aziz2026machine,
title = {{Machine Learning and Deep Learning for Early Disease Detection Across Modalities: A Comprehensive Review of Methods, Applications, and Clinical Translation}},
author = {Ahlem Aziz and Seydi Kacmaz and Azam Isam Abdulkareem Aladwani},
journal = {Diagnostics (Basel, Switzerland)},
year = {2026},
month = sep,
url = {https://europepmc.org/article/PMC/PMC13664333},
note = {CC BY},
}
RIS
TY - JOUR
TI - Machine Learning and Deep Learning for Early Disease Detection Across Modalities: A Comprehensive Review of Methods, Applications, and Clinical Translation
AU - Ahlem Aziz
AU - Seydi Kacmaz
AU - Azam Isam Abdulkareem Aladwani
PY - 2026
DA - 2026/09/25
JO - Diagnostics (Basel, Switzerland)
UR - https://europepmc.org/article/PMC/PMC13664333
SN - 2075-4418
ER -