A hybrid methodology for breast screening and cancer diagnosis using thermography

Resmini, Roger; Silva, Lincoln Faria da; Medeiros, Petrucio R. T.; Araujo, Adriel S.; Muchaluat-Saade, Débora C.; Conci, Aura

doi:10.1016/j.compbiomed.2021.104553

Cited by 23 publications

(8 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Infrared thermography has been used in the medical field for many years. By detecting local temperature abnormalities, diseases such as breast cancer (21,22) and arterial stenosis (23) can be identified. In porcine models, Magnin et al (24) reported that peripheral to central temperature gradient was negatively correlated with arterial pressure (Spearman r = − 0.40, P = 0.002) and cardiac index (Spearman r = −0.36, P = 0.02).…”

Section: Discussionmentioning

confidence: 99%

Infrared thermography-based body-surface thermal inhomogeneity monitoring to assess the severity of hypoperfusion in critically ill patients

Luo

Zhang

Zhao

et al. 2022

Shock

View full text Add to dashboard Cite

Background: Uneven body-surface thermal distribution is a manifestation of hypoperfusion and can be quantified by infrared thermography. Our aim was to investigate whether body-surface thermal inhomogeneity could accurately evaluate the severity of patients at risk of hypoperfusion. Methods: This was a prospective cohort study in which infrared thermography images were taken from unilateral legs of critically ill patients at high risk of hypoperfusion in a cardiac surgical intensive care unit. For each patient, five body-surface thermal inhomogeneity parameters, including standard deviation (SD), kurtosis, skewness, entropy, and low-temperature area rate (LTAR), were calculated. Demographic, clinical, and thermal characteristics of deceased and living patients were compared. The risk of mortality and capillary refill time (CRT) were chosen as the primary outcome and benchmarking parameter for hypoperfusion, respectively. The area under the receiver operating characteristic curve (AUROC) was used to evaluate predictive accuracy. Results: Three hundred seventy-three patients were included, and 55 (14.7%) died during hospital stay. Of inhomogeneity parameters, SD (0.738) and LTAR (0.768) had similar AUROC to CRT (0.757) for assessing mortality risk. Besides, there was a tendency for LTAR (1%-3%-7%) and SD (0.81°C-0.88°C-0.94°C) to increase in normotensive, hypotensive, and shock patients. These thermal parameters are associated with CRT, lactate, and blood pressure. The AUROC of a combined prediction incorporating three thermal inhomogeneity parameters (SD, kurtosis, and entropy) was considerably higher at 0.866. Conclusions: Body-surface thermal inhomogeneity provided a noninvasive and accurate assessment of the severity of critically ill patients at high risk of hypoperfusion.

show abstract

Section: Discussionmentioning

confidence: 99%

Infrared thermography-based body-surface thermal inhomogeneity monitoring to assess the severity of hypoperfusion in critically ill patients

Luo

Zhang

Zhao

et al. 2022

Shock

View full text Add to dashboard Cite

show abstract

“…More proposed approaches are given in Table 5. Resmini et al [123] presented a hybrid methodology for detecting and diagnosing BC using DIT and SIT. e study used the DMR-IR dataset.…”

Section: Research On Breast Thermogram Classificationmentioning

confidence: 99%

A Review on Thermal Imaging-Based Breast Cancer Detection Using Deep Learning

Tsietso

Yahya

Samikannu

2022

Mobile Information Systems

View full text Add to dashboard Cite

Breast cancer is the most common form of cancer in women. Its aggressive nature has made it one of the chief factors of high female mortality. Therefore, this has motivated research to achieve early diagnosis since it is the best strategy for patient survival. Currently, mammography is the gold standard for detecting breast cancer. However, it is expensive, unsuitable for dense breasts, and an invasive process that exposes the patient to radiation. Infrared thermography is gaining popularity as a screening modality for the early detection of breast cancer. It is a noninvasive and cost-effective modality that allows health practitioners to observe the temperature profile of the breast region for signs of cancerous tumors. Deep learning has emerged as a powerful computational tool for the early detection of breast cancer in radiology. As such, this study presents a review that shows existing work on deep learning-based Computer-aided Diagnosis (CADx) systems for breast cancer detection. In the same context, it reflects on classification utilizing breast thermograms. It first provides an overview of infrared thermography, details on available breast thermogram datasets, and then segmentation techniques applied to these thermograms. We also provide a brief overview of deep neural networks. Finally, it reviews works adopting Deep Neural Networks (DNNs) for breast thermogram classification.

show abstract

“…For each of these experiments, several sets of features are computed [38]. Each set aims to evaluate, under different data inputs, the behavior of the system under the combinations of classifiers considered in our methodology.…”

Section: Considerations Related To the Databases And The Segmentation Aspect Of The Input Imagesmentioning

confidence: 99%

Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography

Resmini

Silva

Araujo

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Breast cancer is one of the leading causes of mortality globally, but early diagnosis and treatment can increase the cancer survival rate. In this context, thermography is a suitable approach to help early diagnosis due to the temperature difference between cancerous tissues and healthy neighboring tissues. This work proposes an ensemble method for selecting models and features by combining a Genetic Algorithm (GA) and the Support Vector Machine (SVM) classifier to diagnose breast cancer. Our evaluation demonstrates that the approach presents a significant contribution to the early diagnosis of breast cancer, presenting results with 94.79% Area Under the Receiver Operating Characteristic Curve and 97.18% of Accuracy.

show abstract

A hybrid methodology for breast screening and cancer diagnosis using thermography

Cited by 23 publications

References 71 publications

Infrared thermography-based body-surface thermal inhomogeneity monitoring to assess the severity of hypoperfusion in critically ill patients

Infrared thermography-based body-surface thermal inhomogeneity monitoring to assess the severity of hypoperfusion in critically ill patients

A Review on Thermal Imaging-Based Breast Cancer Detection Using Deep Learning

Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography

Contact Info

Product

Resources

About