Breast cancer is a cancer that can form in the cells of breasts. It is much more common in females than in males. The typical periods of cancer development are during puberty, pregnancy, and breastfeeding. Thermography can be utilized for breast analysis, and provides useful data on the location of hyperthermia and the vascular state of the tissue. Computer-aided diagnosis is an algorithmic approach which can be assistive during routine screening, so that human error in breast analysis for cancer detection is reduced. In early-stage cancer, the accuracy of the assessment then increases, enabling clinicians to make an improved diagnosis of benign versus malignant classification. Herein, we have reviewed thermogram-based computer-aided diagnostic systems developed during the last two decades for breast cancer screening and analysis. We explore the quantitative and qualitative performances of machine learning based approaches, which include segmentation based and feature extraction based methods, dimensionality reduction, and various classification schemes, as proposed in the literature. We also describe the limitations, as well as future requirements to improve current techniques, which can help researchers and clinicians to be apprised of quantitative developments and to plan for the future.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Condensed Matter Physics