Search Results (48)

Thermography was first applied to breast cancer detection in the 1950s but fell out of favor among radiologists due to inconsistent and inconclusive findings in the following decades. Studies conducted in the 21st century using new-generation thermal cameras and computer vision techniques, particularly artificial intelligence, have reported sensitivity and specificity values comparable to those of mammography. However, most radiologists, being unaware of these results, still believe this technique is ineffective, and medical societies advise against using it, even as an adjunct to mammography. In this paper we review recent studies and discuss whether the recommendations of scientific societies are still valid in the light of new evidence. We also propose some ideas for standardizing breast thermography studies that could help make this technique acceptable to the radiology community. Full article

Breast cancer continues to be the most common malignancy among women worldwide, presenting a considerable public health issue. Mammography, though the gold standard for screening, has limitations that catalyzed the advancement of non-invasive, radiation-free alternatives, such as thermal imaging (thermography). This research introduces a novel computer-aided diagnosis (CAD) framework aimed at improving breast cancer detection via thermal imaging. The suggested framework mitigates the limitations of current CAD systems, which frequently utilize intricate convolutional neural network (CNN) structures and resource-intensive preprocessing, by incorporating streamlined CNN designs, transfer learning strategies, and multi-architecture ensemble methods. Features are primarily obtained from various layers of MobileNet, EfficientNetB0, and ShuffleNet architectures to assess the impact of individual layers on classification performance. Following that, feature transformation methods, such as discrete wavelet transform (DWT) and non-negative matrix factorization (NNMF), are employed to diminish feature dimensionality and enhance computational efficiency. Features from all layers of the three CNNs are subsequently incorporated, and the Minimum Redundancy Maximum Relevance (MRMR) algorithm is utilized to determine the most prominent features. Ultimately, support vector machine (SVM) classifiers are employed for classification purposes. The results indicate that integrating features from various CNNs and layers markedly improves performance, attaining a maximum accuracy of 99.4%. Furthermore, the combination of attributes from all three layers of the CNNs, in conjunction with NNMF, attained a maximum accuracy of 99.9% with merely 350 features. This CAD system demonstrates the efficacy of thermal imaging and multi-layer feature amalgamation to enhance non-invasive breast cancer diagnosis by reducing computational requirements through multi-layer feature integration and dimensionality reduction techniques. Full article

Breast cancer remains a leading cause of cancer-related deaths among women worldwide, highlighting the urgent need for early detection. While mammography is the gold standard, it faces cost and accessibility barriers in resource-limited areas. Infrared thermography is a promising cost-effective, non-invasive, painless, and radiation-free alternative that detects tumors by measuring their thermal signatures through thermal infrared radiation. However, challenges persist, including limited clinical validation, lack of Food and Drug Administration (FDA) approval as a primary screening tool, physiological variations among individuals, differing interpretation standards, and a shortage of specialized radiologists. This survey uniquely focuses on integrating texture analysis and machine learning within infrared thermography for breast cancer detection, addressing the existing literature gaps, and noting that this approach achieves high-ranking results. It comprehensively reviews the entire processing pipeline, from image preprocessing and feature extraction to classification and performance assessment. The survey critically analyzes the current limitations, including over-reliance on limited datasets like DMR-IR. By exploring recent advancements, this work aims to reduce radiologists’ workload, enhance diagnostic accuracy, and identify key future research directions in this evolving field. Full article

Background/Objectives: Breast cancer-related lymphedema (BCRL) is a chronic disease with lasting effects, making it one of the most feared sequelae of breast cancer with significant personal and social impacts. Therapeutic exercises play a fundamental role in its treatment. This systematic review aims to provide the most up-to-date findings on the impact of physical exercise on the management of BCRL. Methods: Following the PRISMA statement guidelines, searches were conducted in the Web of Science, Scopus, and Science Direct databases. Results: Sixteen studies published between 2019 and 2024 were analyzed in detail. The combination of strength and aerobic exercises emerged as an effective strategy for both the treatment and prevention of lymphedema, also highlighting the innovative potential of virtual reality. Conclusions: It is essential to emphasize tailoring exercise programs to each patient individually. Additionally, the promising role of thermography as a non-invasive and safe tool for evaluating lymphedema progress is underscored. Full article

Breast cancer remains one of the most prevalent and deadly cancers affecting women worldwide. Early detection is crucial, particularly for younger women, as traditional screening methods like mammography often struggle with accuracy in cases of dense breast tissue. Infrared thermography offers a non-invasive imaging alternative that enhances early detection by capturing subtle thermal variations indicative of breast abnormalities. This study investigates and compares the performance of various deep learning and machine learning models in analyzing thermographic data to classify breast tissue as healthy, benign, or malignant. To maximize detection accuracy, data preprocessing, feature extraction, and dimensionality reduction were implemented to isolate distinguishing characteristics across tissue types. Leveraging advanced feature extraction and visualization techniques inspired by geospatial data methodologies, we evaluated several deep learning architectures and classical classifiers using the DRM-IR and Breast Thermography Mendeley thermal datasets. Among the tested models, the ResNet152 architecture combined with a Support Vector Machine (SVM) classifier delivered the highest performance, achieving 97.62% accuracy, 95.79% precision, 98.53% recall, 94.52% specificity, an F1 score of 97.16%, an area under the curve (AUC) of 99%, a latency of 0.06 s, and CPU utilization of 88.66%. These findings underscore the potential of integrating infrared thermography with advanced deep learning and machine learning approaches to significantly improve the accuracy and efficiency of breast cancer detection, supporting its role as a valuable tool for early diagnosis. Full article

Breast cancer is one of the leading causes of death for women worldwide, and early detection can help reduce the death rate. Infrared thermography has gained popularity as a non-invasive and rapid method for detecting this pathology and can be further enhanced by applying neural networks to extract spatial and even temporal data derived from breast thermographic images if they are acquired sequentially. In this study, we evaluated hybrid convolutional-recurrent neural network (CNN-RNN) models based on five state-of-the-art pre-trained CNN architectures coupled with three RNNs to discern tumor abnormalities in dynamic breast thermographic images. The hybrid architecture that achieved the best performance for detecting breast cancer was VGG16-LSTM, which showed accuracy (ACC), sensitivity (SENS), and specificity (SPEC) of 95.72%, 92.76%, and 98.68%, respectively, with a CPU runtime of 3.9 s. However, the hybrid architecture that showed the fastest CPU runtime was AlexNet-RNN with 0.61 s, although with lower performance (ACC: 80.59%, SENS: 68.52%, SPEC: 92.76%), but still superior to AlexNet (ACC: 69.41%, SENS: 52.63%, SPEC: 86.18%) with 0.44 s. Our findings show that hybrid CNN-RNN models outperform stand-alone CNN models, indicating that temporal data recovery from dynamic breast thermographs is possible without significantly compromising classifier runtime. Full article

Background/Objectives: Breast cancer-related lymphedema (BCRL) is a chronic condition that has early diagnosis as a critical component for proper treatment. Thermography, a non-invasive imaging method, is considered a promising complementary tool for the diagnosis and monitoring of BCRL, especially in subclinical stages. The present study aimed to evaluate the intra- and inter-examiner reproducibility of thermography for measuring the skin temperature of the upper limbs (UL) of women with and without BCRL. Methods: This study, conducted with women who underwent a unilateral mastectomy, assessed BCRL using indirect volumetry. Maximum, minimum, and mean skin temperatures were measured in five regions of interest (ROI) of each UL (C1, C2, C3, C4, and Cup) in four different postures. Reproducibility measures were assessed using an intraclass correlation coefficient, 95% confidence interval, and coefficient of variation. Results: The sample comprised 30 women; 14 were diagnosed with BCRL. A total of 120 thermograms were recorded in different postures, and 3600 ROI were analyzed in the UL with and without BRCL. The intraclass correlation coefficient of the analyses indicated intra- and inter-examiner reproducibility from good to excellent (0.82 to 1.00) for all skin temperatures evaluated (maximum, minimum, and mean). The coefficient of variation for all measures was below 10%, indicating low variability. Conclusions: Our findings demonstrate that thermography shows good-to-excellent reproducibility across multiple postures and regions of interest, reinforcing its potential as a non-invasive and reliable method for assessing lymphedema in breast cancer survivors. This study establishes a foundation for incorporating thermography into clinical practice for early BCRL detection, particularly in subclinical stages, thus improving patient management and outcomes. Full article

Advances in the early detection of breast cancer and treatment improvements have significantly increased survival rates. Traditional screening methods, including mammography, MRI, ultrasound, and biopsies, while effective, often come with high costs and risks. Recently, thermal imaging has gained attention due to its minimal risks compared to mammography, although it is not widely adopted as a primary detection tool since it depends on identifying skin temperature changes and lesions. The advent of machine learning (ML) and deep learning (DL) has enhanced the effectiveness of breast cancer detection and diagnosis using this technology. In this study, a novel interpretable computer aided diagnosis (CAD) system for breast cancer detection is proposed, leveraging Explainable Artificial Intelligence (XAI) throughout its various phases. To achieve these goals, we proposed a new multi-objective optimization approach named the Hybrid Particle Swarm Optimization algorithm (HPSO) and Hybrid Spider Monkey Optimization algorithm (HSMO). These algorithms simultaneously combined the continuous and binary representations of PSO and SMO to effectively manage trade-offs between accuracy, feature selection, and hyperparameter tuning. We evaluated several CAD models and investigated the impact of handcrafted methods such as Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gabor Filters, and Edge Detection. We further shed light on the effect of feature selection and optimization on feature attribution and model decision-making processes using the SHapley Additive exPlanations (SHAP) framework, with a particular emphasis on cancer classification using the DMR-IR dataset. The results of our experiments demonstrate in all trials that the performance of the model is improved. With HSMO, our models achieved an accuracy of 98.27% and F1-score of 98.15% while selecting only 25.78% of the HOG features. This approach not only boosts the performance of CAD models but also ensures comprehensive interpretability. This method emerges as a promising and transparent tool for early breast cancer diagnosis. Full article

Background/Objectives: Infrared thermography (IRT) is an imaging technique used in clinical practice to detect changes in skin temperature caused by several dysfunctions, including breast cancer-related lymphedema (BCRL). Thus, the present study aimed to assess the reproducibility and accuracy of IRT in diagnosing BCRL. Methods: This cross-sectional study included participants who underwent a unilateral mastectomy and used indirect volumetry for lymphedema detection. IRT analysis was recorded in four positions, analyzing maximum, mean, and minimum temperatures, as well as the temperature differences between the upper limbs. The analysis encompassed reliability, agreement, accuracy, and the establishment of cut-off points for sensitivity and specificity. A total of 88 upper limbs were included; 176 thermograms were captured, and 1056 regions of interest were analyzed. Results: IRT presented excellent intra- and inter-rater reproducibility and reliability with excellent intraclass correlation coefficient values (0.99 to 1.00). In addition, this assessment reached a sensitivity of 85% and a specificity of 56%; the cut-off point considered a temperature difference of −0.45 °C. Conclusions: IRT was a reliable and reproducible assessment, and the temperature difference between the upper limbs evidenced moderate accuracy. Thus, IRT is recommended as a complementary technique for detecting BCRL. Full article

Breast cancer remains a global health problem requiring effective diagnostic methods for early detection, in order to achieve the World Health Organization’s ultimate goal of breast self-examination. A literature review indicates the urgency of improving diagnostic methods and identifies thermography as a promising, cost-effective, non-invasive, adjunctive, and complementary detection method. This research explores the potential of using machine learning techniques, specifically Bayesian networks combined with convolutional neural networks, to improve possible breast cancer diagnosis at early stages. Explainable artificial intelligence aims to clarify the reasoning behind any output of artificial neural network-based models. The proposed integration adds interpretability of the diagnosis, which is particularly significant for a medical diagnosis. We constructed two diagnostic expert models: Model A and Model B. In this research, Model A, combining thermal images after the explainable artificial intelligence process together with medical records, achieved an accuracy of 84.07%, while model B, which also includes a convolutional neural network prediction, achieved an accuracy of 90.93%. These results demonstrate the potential of explainable artificial intelligence to improve possible breast cancer diagnosis, with very high accuracy. Full article

Background: Perforator mapping is a mandatory tool for the preoperative planning of a microsurgical free flap, especially in breast reconstruction. Numerous methods for mapping have been described. In this study, we investigate the combined use of Dynamic Infrared Thermography (DIRT) and Colour Doppler Ultrasonography (CDUS) only to see whether it can eliminate the need for Computed Tomography Angiography (CTA). Methods: A prospective study was conducted on 33 patients with deep inferior epigastric perforator (DIEP) flaps for breast reconstruction. DIRT, followed by CDUS and CTA, was performed preoperatively and perforators were confirmed intraoperatively. Results: From 135 hot spots found on DIRT, 123 perforators were confirmed by CDUS (91.11%). A total of 86.66% of the perforator vessels detected on CTA have their correspondent on DIRT, while 95.12% have their correspondent on CDUS. No statistically significant difference (p > 0.05) was found comparing DIRT vs. CTA and CDU vs. CTA. The average DIRT time was 121.54 s and CDUS 232.09 s. The mean sensitivity for DIRT was 95.72% and 93.16% for CDUS. Conclusion: DIRT combined with CDUS can precisely and efficiently identify suitable perforators without the need for CTA in DIEP breast reconstruction. Full article

Breast cancer detection at an early stage is crucial for improving patient survival rates. This work introduces an innovative thermal imaging prototype that incorporates compression techniques inspired by mammography equipment. The prototype offers a radiation-free and precise cancer diagnosis. By integrating compression and illumination methods, thermal picture quality has increased, and the accuracy of classification has improved. Essential components of the suggested thermography device include an equipment body, plates, motors, pressure sensors, light sources, and a thermal camera. We created a 3D model of the gadget using the SolidWorks software 2020 package. Furthermore, the classification research employed both cancer and normal images from the experimental results to validate the efficacy of the suggested system. We employed preprocessing and segmentation methods on the obtained dataset. We successfully categorized the thermal pictures using various classifiers and examined their performance. The logistic regression model showed excellent performance, achieving an accuracy of 0.976, F1 score of 0.977, precision of 1.000, and recall of 0.995. This indicates a high level of accuracy in correctly classifying thermal abnormalities associated with breast cancer. The proposed prototype serves as a highly effective tool for conducting initial investigations into breast cancer detection, offering potential advancements in early-stage diagnosis, and improving patient survival rates. Full article

The present review aims to analyze the application of infrared thermal imaging, aided by bio-heat models, as a tool for the diagnosis of skin and breast cancers. The state of the art of the related technical procedures, bio-heat transfer modeling, and thermogram post-processing methods is comprehensively reviewed. Once the thermal signatures of different malignant diseases are described, the updated thermographic techniques (steady-state and dynamic) used for cancer diagnosis are discussed in detail, along with the recommended best practices to ensure the most significant thermal contrast observable between the cancerous and healthy tissues. Regarding the dynamic techniques, particular emphasis is placed on innovative methods, such as lock-in thermography, thermal wave imaging, and rotational breast thermography. Forward and inverse modeling techniques for the bio-heat transfer in skin and breast tissues, supporting the thermographic examination and providing accurate data for training artificial intelligence (AI) algorithms, are reported with a special focus on real breast geometry-based 3D models. In terms of inverse techniques, different data processing algorithms to retrieve thermophysical parameters and growth features of tumor lesions are mentioned. Post-processing of infrared images is also described, citing both conventional processing procedures and applications of AI algorithms for tumor detection. Full article

Thermography has found extensive application as a supplementary diagnostic tool in breast cancer diagnosis, notably complementing the clinical breast exam (CBE). Within dynamic thermography, matrix factorization methods have demonstrated their utility in accentuating thermal heterogeneities by generating thermal basis vectors. A significant challenge in such approaches is to identify the leading thermal basis vector that effectively captures predominant thermal patterns. Embedding methods are used to fuse multiple projected basis vectors onto a single basis for the extraction of the thermal features, known as thermomics. In this study, we introduce Rayleigh embedding to project thermal basis vectors obtained from factorization techniques into a lower-dimensional space, highlighting thermal patterns. This enhances the reliability of the thermal system, thereby assisting in CBE. The best results of the embedding method combining clinical information and demographics yield 82.9% (66.7%, 86.7%) using a random forest. The results demonstrated promising preliminary outcomes, leading to the early detection of breast abnormalities, and can serve as a non-invasive tool to aid CBE. Full article

Breast reconstruction is an essential component in the multidisciplinary management of breast cancer patients. Over the years, preoperative planning has played a pivotal role in assisting surgeons in planning operative decisions prior to the day of surgery. The evolution of preoperative planning can be traced back to the introduction of modalities such as ultrasound and colour duplex ultrasonography, enabling surgeons to evaluate the donor site’s vasculature and thereby plan operations more accurately. However, the limitations of these techniques paved the way for the implementation of modern three-dimensional imaging technologies. With the advancements in 3D imaging, including computed tomography and magnetic resonance imaging, surgeons gained the ability to obtain detailed anatomical information. Moreover, numerous adjuncts have been developed to aid in the planning process. The integration of 3D-printing technologies has made significant contributions, enabling surgeons to create complex haptic models of the underlying anatomy. Direct infrared thermography provides a non-invasive, visual assessment of abdominal wall vascular physiology. Additionally, augmented reality technologies are poised to reshape surgical planning by providing an immersive and interactive environment for surgeons to visualize and manipulate 3D reconstructions. Still, the future of preoperative planning in breast reconstruction holds immense promise. Most recently, artificial intelligence algorithms, utilising machine learning and deep learning techniques, have the potential to automate and enhance preoperative planning processes. This review provides a comprehensive assessment of the history of innovation in preoperative planning for breast reconstruction, while also outlining key future directions, and the impact of artificial intelligence in this field. Full article