Early Detection of Acute Myeloid Leukemia (AML) Using YOLOv12 Deep Learning Model

2026-04-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Health & Medical Research · Depth: Expert, quick

Summary

A study utilized the YOLOv12 deep learning model for the early detection and multiclass classification of Acute Myeloid Leukemia (AML) cells, a challenging task due to visual similarities between cell types. Researchers applied two segmentation approaches, one cell-based and one nucleus-based, using Hue channel and Otsu thresholding techniques for image preprocessing. Experiments showed that the YOLOv12 model, when combined with Otsu thresholding on cell-based segmentation, achieved a 99.3% accuracy on both validation and test datasets. This high accuracy demonstrates the model's effectiveness in distinguishing various AML cell types, which is critical for early diagnosis of this life-threatening blood cancer.

Key takeaway

For Computer Vision Engineers developing diagnostic tools for hematological cancers, this research indicates that integrating YOLOv12 with Otsu thresholding for cell-based segmentation can yield highly accurate AML cell classification. You should consider this specific preprocessing and model combination to enhance the performance of your early detection systems, potentially improving diagnostic reliability and speed for critical conditions.

Key insights

YOLOv12 with Otsu thresholding on cell-based segmentation achieves 99.3% accuracy for AML cell classification.

Principles

• Early detection improves AML outcomes.
• Segmentation enhances deep learning classification.

Method

The method involves preprocessing images with Hue channel and Otsu thresholding, followed by cell-based or nucleus-based segmentation, and then classification using a YOLOv12 deep learning model.

In practice

• Apply Otsu thresholding for cell segmentation.
• Use YOLOv12 for medical image classification.

Topics

• Acute Myeloid Leukemia
• YOLOv12
• Deep Learning
• Cell Segmentation
• Otsu Thresholding

Best for: AI Scientist, Research Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.