Early Detection of Acute Myeloid Leukemia (AML) Using YOLOv12 Deep Learning Model
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914483336642560 |
|---|---|
| author | Ahmed, Enas E. Aly, Salah A. Moner, Mayar |
| author_facet | Ahmed, Enas E. Aly, Salah A. Moner, Mayar |
| contents | Acute Myeloid Leukemia (AML) is one of the most life-threatening type of blood cancers, and its accurate classification is considered and remains a challenging task due to the visual similarity between various cell types. This study addresses the classification of the multiclasses of AML cells Utilizing YOLOv12 deep learning model. We applied two segmentation approaches based on cell and nucleus features, using Hue channel and Otsu thresholding techniques to preprocess the images prior to classification. Our experiments demonstrate that YOLOv12 with Otsu thresholding on cell-based segmentation achieved the highest level of validation and test accuracy, both reaching 99.3%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_16082 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Early Detection of Acute Myeloid Leukemia (AML) Using YOLOv12 Deep Learning Model Ahmed, Enas E. Aly, Salah A. Moner, Mayar Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Acute Myeloid Leukemia (AML) is one of the most life-threatening type of blood cancers, and its accurate classification is considered and remains a challenging task due to the visual similarity between various cell types. This study addresses the classification of the multiclasses of AML cells Utilizing YOLOv12 deep learning model. We applied two segmentation approaches based on cell and nucleus features, using Hue channel and Otsu thresholding techniques to preprocess the images prior to classification. Our experiments demonstrate that YOLOv12 with Otsu thresholding on cell-based segmentation achieved the highest level of validation and test accuracy, both reaching 99.3%. |
| title | Early Detection of Acute Myeloid Leukemia (AML) Using YOLOv12 Deep Learning Model |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.16082 |