A hybrid methodology for breast screening and cancer diagnosis using thermography. (August 2021)

• Record Type: Journal Article
• Authors: Resmini, Roger; Faria da Silva, Lincoln; Medeiros, Petrucio R.T.; Araujo, Adriel S.; Muchaluat-Saade, Débora C.; Conci, Aura
• Is Part Of: Computers in biology and medicine. Volume 135(2021)
• Publication Date: 2021-08
• Subjects: Breast cancer -- Screening -- Diagnosis -- Thermography -- Texture -- GLCM; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104553
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Breast cancer is the second most common cancer in the world. Early diagnosis and treatment increase the patient's chances of healing. The temperature of cancerous tissues is generally different from that of healthy neighboring tissues, making thermography an option to be considered in the fight against cancer because it does not use ionizing radiation, venous access, or any other invasive process, presenting no damage or risk to the patient. In this paper, we propose a hybrid computational method using the Dynamic Infrared Thermography (DIT) and Static Infrared Thermography (SIT) for abnormality screening and diagnosis of malignant tumor (cancer), applying supervised and unsupervised machine learning techniques. We use the area under receiver operating characteristic curve, sensitivity, specificity, and accuracy as performance measures to compare the hybrid methodology with previous work in the literature. The K-Star classifier achieved accuracy of 99% in the screening phase using DIT images. The Support Vector Machines (SVM) classifier applied on SIT images yielded accuracy of 95% in the diagnosis of cancer. The results confirm the potential of the proposed approaches for screening and diagnosis of breast cancer. Graphical abstract: Image 1 Highlights: All the infrared examinations are from a public available dataset. Evaluates the use of breast thermography for cancer screening and diagnosis. We use the K-Star and SVM classifiers for cancer screening and diagnosis, respectively. Temperature time-series from DIT images reach 99% accuracy in cancer screening. For diagnosis, we use texture features from SIT images and achieve 95% accuracy.