Deep Learning-Based Medical Object Detection: A Survey
Mohammadreza Saraei, Mehrshad Lalinia, Eung-Joo Lee
Abstract
Recent advancements in medical object detection (MOD) have been propelled by the rapid evolution of deep learning (DL) technologies, revolutionizing medical imaging and diagnostic workflows. This survey comprehensively reviews various studies across diverse imaging modalities, including X-Ray, CT, MRI, Ultrasound, and Histopathology. Notable improvements include integrating You Only Look Once (YOLO)-based architectures, Vision Transformers (ViT), and hybrid attention mechanisms, significantly enhancing detection accuracy and efficiency. Standout models, such as YOLOv8m, Hybrid YOLO-NAS, and YOLOv4+ViT, have demonstrated exceptional performance, achieving mean average precision (mAP) scores between 98.6% and 99.5%. These advancements leverage sophisticated features like Cross-Stage Partial (CSP) networks, Spatial Pyramid Pooling (SPP), and Bi-Directional Feature Pyramid Networks (BiFPN) to improve feature extraction and detection in medical images. Despite these successes, challenges remain in adapting these models to resource-limited settings and ensuring their outputs are interpretable for clinicians. This survey aims to bridge the gap between theoretical progress and practical implementation by aligning cutting-edge technological developments with clinical demands. It provides a certain roadmap for future innovation in MOD, with the overarching goal of improving patient care through enhanced diagnostic capabilities.