Clinical Thermography for Breast Cancer Screening: A Systematic Review on Image Acquisition, Segmentation, and Classification

Abstract

There is a life after breast cancer. The prerequisite is early detection. Breast cancer is curable when detected early, tiny, and has not spread – regular screening aids in early detection. Clinical Thermography and artificial intelligence are potentially a good fit for early breast cancer screening. This survey paper presents a systematic review of artificial intelligence-based breast cancer screening using thermal infrared cameras. Initially, we will present the qualitative analysis of the existing literature regarding the trend and distribution. This review manuscript will then explore the literature about infrared thermal image acquisition and storage techniques. We will then highlight various segmentation techniques used for processing infrared thermal images. This paper presents the experimental results of the traditional image processing and deep learning-based segmentation techniques available in the literature using infrared breast thermal images. We then summarize the works that have used artificial intelligence to segment and classify infrared thermal images. The existing literature shows opportunities to explore the area of explainable artificial intelligence (AI). Explainable AI will make clinical Thermography into assistive technology for the medical community.

Keywords:

• Biomedical imaging
• Breast cancer screening
• Clinical thermography
• Computer vision problems
• Infrared image classification
• Infrared image segmentation
• Infrared imaging

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

R. Kaushik

R Kaushik is a research scholar at the Indian Institute of Information Technology, Design, and Manufacturing (IIITD & M – Kancheepuram). He has around two decades of experience in the IT industry. He is also the AVP – Of product research at resileo – labs. Kaushik works in biomedical image processing, big data analytics, and machine learning. He is working toward developing new analysis and image-processing methodologies for quantitative infrared imaging, mainly on the applications of machine learning and computer vision in medical imaging for diagnosis and grading of diseases such as cancer/tumors.

B. Sivaselvan

B Sivaselavan is a professor at the Department of Computer Science and Engineering, IIITD & M – Kancheepuram. His areas of interest include knowledge & data engineering, data analytics, human-computer interaction, computer vision, image processing, and machine learning. He completed his MTech in computer science and Engineering from IIT Madras and PhD from NIT Trichy. Sivaselavan was a recipient of AICTE’s National Doctoral Fellowship. He leads several projects on data analytics and machine vision projects at IIITD & M. Email: [email protected]

V. Kamakoti

V Kamakoti is currently the director of the Indian Institute of Technology Madras (IIT Madras). He is a professor at the Department of Computer Science and Engineering. His areas of interest include computer architecture, secure systems engineering, VLSI design, and cyber-physical systems for biomedical devices. Kamakoti led the research team that designed India’s first indigenously-developed microprocessor, ‘Shakti’. He completed his PhD at the Computer Science and Engineering Department at IIT Madras. Email: [email protected]