  Object detection and localization are crucial tasks for biomedical image
analysis, particularly in the field of hematology where the detection and
recognition of blood cells are essential for diagnosis and treatment decisions.
While attention-based methods have shown significant progress in object
detection in various domains, their application in medical object detection has
been limited due to the unique challenges posed by medical imaging datasets. To
address this issue, we propose ADA-YOLO, a light-weight yet effective method
for medical object detection that integrates attention-based mechanisms with
the YOLOv8 architecture. Our proposed method leverages the dynamic feature
localisation and parallel regression for computer vision tasks through
\textit{adaptive head} module. Empirical experiments were conducted on the
Blood Cell Count and Detection (BCCD) dataset to evaluate the effectiveness of
ADA-YOLO. The results showed that ADA-YOLO outperforms the YOLOv8 model in mAP
(mean average precision) on the BCCD dataset by using more than 3 times less
space than YOLOv8. This indicates that our proposed method is effective.
Moreover, the light-weight nature of our proposed method makes it suitable for
deployment in resource-constrained environments such as mobile devices or edge
computing systems. which could ultimately lead to improved diagnosis and
treatment outcomes in the field of hematology.
