ADA-YOLO: Dynamic Fusion of YOLOv8 and Adaptive Heads for Precise Image Detection and Diagnosis

TL;DR

ADA-YOLO is a lightweight, adaptive object detection method that enhances YOLOv8 for precise blood cell detection, outperforming existing models in accuracy and efficiency, especially suitable for resource-limited medical settings.

Contribution

The paper introduces ADA-YOLO, a novel adaptive fusion approach combining attention mechanisms with YOLOv8, optimized for medical image detection with improved accuracy and reduced model size.

Findings

ADA-YOLO outperforms YOLOv8 in mAP on BCCD dataset.

ADA-YOLO uses over 3 times less space than YOLOv8.

The method is suitable for deployment in resource-constrained environments.

Abstract

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.