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Deep learning-based object detection algorithms in medical imaging: Systematic review.

Carina Albuquerque¹, Roberto Henriques¹, Mauro Castelli¹

¹NOVA Information Management School, Lisboa, Portugal.

|January 6, 2025

Summary

This summary is machine-generated.

Deep learning object detection in medical images is rapidly advancing, showing great potential across various imaging techniques and anatomical areas. Continued research is vital for optimizing these powerful tools for clinical applications.

Keywords:

Bibliometric analysis Deep learning Medical imaging Object detection Qualitative analysis Quantitative analysis

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Area of Science:

Computer Science
Medical Imaging
Artificial Intelligence

Background:

Deep Learning (DL) has significantly advanced various fields, particularly in medical image analysis for tasks like segmentation, detection, and classification.
DL-based object recognition in medical imaging is gaining traction due to its potential to improve diagnostic accuracy and efficiency.

Purpose of the Study:

To provide an overview of Deep Learning-based object recognition in medical images.
To explore recent methods, imaging techniques, and anatomical applications of DL object detection.
To analyze trends and identify future research directions in this domain.

Main Methods:

A systematic literature review using PRISMA guidelines.
Quantitative and qualitative analysis of publications based on citation rates.
Examination of DL object detection utilization across different imaging modalities and anatomical domains.

Main Results:

A consistent increase in the use of DL-based object detection models in medical imaging was observed.
Research is most active in the US, China, and Japan, primarily within Medicine and Computer Science.
DL methods are adaptable, applied to CR scans, pathology images, and endoscopic imaging, with notable use in digital pathology and microscopy.

Conclusions:

Deep learning object detection shows significant, yet unexploited, potential in medical image analysis.
Varied dataset sizes, a prevalence of private datasets, and a scarcity of prospective studies present challenges.
Ongoing research and application-specific optimization are crucial for advancing DL in medical imaging.