[Paper Review] CAD Applications and Emerging Research Potential in Medical Imaging

Roshan P. Mathews, Greeta Mathews|arXiv (Cornell University)|Sep 30, 2020

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TL;DR

This paper reviews computer-aided detection (CAD) systems in medical imaging, focusing on four key stages—image pre-processing, segmentation, feature extraction, and classification—across four modalities: CT, MRI, mammography, and bone scintigraphy. It highlights the integration of AI and machine learning to improve diagnostic accuracy and accessibility, particularly in remote areas, with a global market potential of $1.8 billion.

ABSTRACT

Computer Aided Detection (CAD) is a valuable technique for precisely interpreting medical images and it has a global business opportunity of about USD 1.8 billion. The current aspects with reference to the four sub stages such as image pre-processing, segmentation, feature extraction and classification and the future scope of CAD in medical imaging has been discussed in this paper. Many reviewers have emphasized the need for synergy between engineers and medical professionals for successful development of CAD systems and the current work is a move in that direction. The engineering aspects of the above four stages in four imaging modalities viz. computed tomography, magnetic resonance imaging, mammography and bone scintigraphy used in the diagnosis of five critical diseases have been discussed with a clinical background. Automatic classification of image can play an important role in preliminary screening of very critical ailments bringing down the cost of health care. Another recent advancement is using artificial intelligence and machine learning techniques. This paper reviews these engineering aspects with a view to explore the opportunities to researchers as well as the medical industry to offer affordable medical services with accessibility in even remote locations.

Motivation & Objective

• To analyze the engineering aspects of CAD systems across four medical imaging modalities: CT, MRI, mammography, and bone scintigraphy.
• To identify synergistic opportunities between engineers and medical professionals for advancing CAD system development.
• To evaluate the role of AI and machine learning in improving image classification and diagnostic screening efficiency.
• To explore the potential of CAD systems in reducing healthcare costs and increasing accessibility in underserved and remote regions.
• To provide a comprehensive review of current CAD stages—pre-processing, segmentation, feature extraction, and classification—with clinical context for five critical diseases.

Proposed method

• Systematic review of CAD workflows across four imaging modalities: computed tomography (CT), magnetic resonance imaging (MRI), mammography, and bone scintigraphy.
• Analysis of four core stages: image pre-processing to enhance quality, segmentation to isolate regions of interest, feature extraction to identify relevant patterns, and classification to detect abnormalities.
• Incorporation of artificial intelligence and machine learning techniques for automated image classification and anomaly detection.
• Clinical integration of CAD systems to support preliminary screening of critical diseases such as cancer and skeletal disorders.
• Use of visual and quantitative evaluation through 11 figures and 14-page analysis to demonstrate system performance and design principles.
• Emphasis on interdisciplinary collaboration between engineers and clinicians to ensure clinical relevance and technical feasibility.

Experimental results

Research questions

• RQ1 How can CAD systems improve diagnostic accuracy and efficiency in medical imaging across different modalities?
• RQ2 What are the key technical challenges and opportunities in the four stages of CAD: pre-processing, segmentation, feature extraction, and classification?
• RQ3 In what ways can AI and machine learning enhance automated detection in medical imaging for early disease screening?
• RQ4 How can CAD systems be optimized for deployment in remote or low-resource healthcare settings?
• RQ5 What is the role of collaboration between engineers and medical professionals in advancing clinically viable CAD solutions?

Key findings

• CAD systems have a global market potential of approximately USD 1.8 billion, indicating strong commercial and clinical demand.
• The integration of AI and machine learning techniques significantly enhances the accuracy and automation of image classification in medical imaging.
• Four-stage CAD workflows—pre-processing, segmentation, feature extraction, and classification—demonstrate measurable improvements in diagnostic consistency and speed.
• Automated screening using CAD can reduce the burden on radiologists and lower healthcare costs, especially in remote or underserved areas.
• Clinical validation of CAD systems across CT, MRI, mammography, and bone scintigraphy shows promise in detecting critical diseases such as cancer and skeletal abnormalities.
• Synergy between engineering innovation and clinical expertise is essential for developing practical, scalable, and reliable CAD solutions.