Summary of Early Diagnosis Of Acute Lymphoblastic Leukemia Using Yolov8 and Yolov11 Deep Learning Models, by Alaa Awad et al.
Early Diagnosis of Acute Lymphoblastic Leukemia Using YOLOv8 and YOLOv11 Deep Learning Models
by Alaa Awad, Salah A. Aly
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a deep learning-based approach for detecting Acute Lymphoblastic Leukemia (ALL) using advanced image processing techniques. Specifically, it evaluates the performance of YOLO models, including YOLOv8 and YOLOv11, to identify malignant and benign white blood cells, as well as different stages of ALL, including early stages. The study also demonstrates the ability to detect hematogones, which are frequently misclassified as ALL. With an accuracy rate of 98.8%, this research highlights the potential of these algorithms for robust and precise leukemia detection across diverse datasets and conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special computer programs called YOLO models to find blood cancer, Acute Lymphoblastic Leukemia (ALL). It looks at how well these programs can tell apart healthy and unhealthy white blood cells. The research also tries to identify different stages of ALL, which is important because it helps doctors make better decisions about treatment. The study shows that these computer programs are very good at finding certain types of cells called hematogones, which can look like cancer but aren’t. This means the algorithms could help doctors detect leukemia more accurately. |
Keywords
» Artificial intelligence » Deep learning » Yolo
