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Quadrant wise segmentation of breasts: Upper-Outer (UOQ), Upper-Inner (UIQ), Lower-Outer (LOQ), Lower-Inner (LIQ)
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Breast cancer is not preventable. To reduce the death rate and improve the survival chances of breast cancer patients, early and accurate detection is the only panacea. Delay in diagnosis of this disease causes 60% of deaths. Thermal imaging is a low-risk modality for early breast cancer decision making without injecting any form of energy into the...
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In this work, a novel image segmentation method in thermographic images using the computer vision algorithm, active contours, is presented. These studies were done on thermal images obtained from pulse thermography. Many aerospace components are made of composite laminates. De-bonds/de-laminations set in during the manufacturing or in-service stage...
Citations
... Several studies have investigated the use of IRT in epithelial neoplasms as a complementary tool in the diagnostic process of skin tumors in various regions of the body and to improve accuracy and precision in decision-making regarding the most appropriate treatment [10][11][12]. However, to date, no study has systematically assessed the effectiveness of such method as a diagnostic tool for skin neoplasms in the head and neck region. ...
The aim of this study was to assess, through a systematic review, the status of infrared thermography (IRT) as a diagnostic tool for skin neoplasms of the head and neck region and in order to validate its effectiveness in differentiating benign and malignant lesions. A search was carried out in the LILACS, PubMed/MEDLINE, SCOPUS, Web of Science and EMBASE databases including studies published between 2004 and 2024, written in the Latin-Roman alphabet. Accuracy studies with patients aged 18 years or over presenting benign and malignant lesions in the head and neck region that evaluated the performance of IRT in differentiating these lesions were included. Lesions of mesenchymal origin and studies that did not mention histopathological diagnosis were excluded. The systematic review protocol was registered in the PROSPERO database (CRD42023416079). Reviewers independently analyzed titles, abstracts, and full-texts. After extracting data, the risk of bias of the selected studies was assessed using the QUADAS − 2 tool. Results were narratively synthesized and the certainty of evidence was measured using the GRADE approach. The search resulted in 1,587 records and three studies were included. Only one of the assessed studies used static IRT, while the other two studies used cold thermal stress. All studies had an uncertain risk of bias. In general, studies have shown wide variation in the accuracy of IRT for differentiating between malignant and benign lesions, with a low level of certainty in the evidence for both specificity and sensitivity.
... Accurately diagnosing and predicting diseases using digital image analysis is a hot topic [5]. In some situations, mainly when predicting hazardous diseases, the health status of an individual determined by a medical examination may not be reliable [6]. The digital imaging technique is introduced to decrease the death rate from breast cancer to assess the illness risk in a particular person [7]. ...
... As a result, SMbRNS segmentation and classification operations are processed using Eq. (6). ...
The most common and rapidly spreading disease in the world is breast cancer. Most cases of breast cancer are observed in females. Breast cancer is controlled with early detection. Therefore, early detection and categorization of breast cancer are essential to enable patients to take the right course of treatment. Early discovery helps to manage many cases and lower the death rate. In this study, a brand-new Spider Monkey-based Recurrent Neural System (SMbRNS) is created for predicting breast cancer cells in an early stage. Breast mammography images are used in this instance as the dataset for the system. The breast dataset is also analyzed using the established SMbRNS function to detect and segregate the breast cancer-afflicted region efficiently. The developed model aims to enhance the segmented breast cancer results using spider monkey fitness. The developed method computes the chance of breast cancer using the dataset; segmented images are used for monitoring. Additionally, the Python code used to perform this strategy allows for evaluating the created model parameters against earlier research. The experimental results are validated with other prevailing models regarding the accuracy, precision, sensitivity, specificity, and F1-score to prove the efficiency. The designed model gained 99.82% accuracy and 99.12% precision for segmenting breast cancer. The current study model produces mammography with better accuracy for the segmentation of breast cancer.
... There is a statistically significant difference in the mean value of the pathological side temperature between the different degrees of tumor in malignant tumors (p<0.05), a greater temperature gradient was confirmed in high-grade tumors. od to consider for detection and monitoring [6,7,[11][12][13][15][16][17]. ...
We evaluate the role of infrared thermography in the study of soft tissue tumors and their ability to distinguish between benign lesions and potentially malignant lesions.
... One of the key advantages of thermal imaging is its non-invasiveness. Unlike traditional diagnostic methods, which may involve painful procedures or exposure to ionizing radiation, thermal imaging simply requires the use of a specialized camera to capture thermal images of the body surface [3] . This makes it a more comfortable and safer option for patients, especially for routine screenings and long-term monitoring. ...
Problem: There is a need for effective and non-invasive techniques for early cancer detection to improve treatment outcomes and patient care. Motivation: This research explores the potential of thermal imaging as a non-invasive technique for cancer detection. Aim: The aim of this study is to investigate thermal imaging as a valuable tool for early cancer detection and its potential to enhance treatment outcomes and patient care. Methodology: The paper discusses the principles of thermal imaging, its advantages and limitations, and its application to various types of cancer. It also presents a review of recent studies in the field. Main results: The findings suggest that thermal imaging holds promise as a valuable tool for early cancer detection. Further impact of those results: The potential application of thermal imaging in cancer detection could lead to improved treatment outcomes and enhance overall patient care. The article also highlights the challenges and future prospects of thermal imaging in this domain.
... Zhou and Jiang (2003) used the combination of 4.5c algorithm and neural networks and were able to classify breast cancer patients with 94% accuracy. Hakim and Awale (2020) used the combination of SOM and MLP neural network to determine whether breast cancer is benign or malignant. In SOM network, competitive learning method is used for training. ...
Introduction
Breast cancer is known as the most common type of cancer in women, and this has raised the importance of its diagnosis in medical science as one of the most important issues. In addition to reducing costs, the diagnosis of benign or malignant breast cancer is very important in determining the treatment method.
Objective
The purpose of this paper is to present a model based on data mining techniques including feature selection and ensemble classification that can accurately predict breast cancer patients in the early stages.
Methodology
The proposed breast cancer detection model is developed by joining Adaptive Differential Evolution (ADE) algorithm for feature selection and Learning Vector Quantization (LVQ) neural network for classification. Our proposed model as ADE–LVQ has the ability to automatically and quickly diagnose breast cancer patients into two classes, benign and malignant. As a new evolutionary approach, ADE performs optimal configuration for LVQ neural network in addition to selecting effective features from breast cancer data. Meanwhile, we configure an ensemble classification technique based on LVQ, which significantly improves the prediction performance.
Results
ADE–LVQ has been analyzed from different perspectives on different datasets from Wisconsin breast cancer database. We apply different approaches to handle missing values and improve data quality on this database. The results of the simulations showed that the ADE–LVQ model is more successful than the equivalent and state-of-the-art models in diagnosing breast cancer patients. Also, ADE–LVQ provides better performance with less complexity, considering feature selection and ensemble learning. In particular, ADE–LVQ improves accuracy (up to 3.4%) and runtime (up to 2.3%) on average compared to the existing best method.
Conclusion
Combined methods based on data mining techniques for breast cancer diagnosis can help doctors in making better decisions for disease treatment.
... The medical use of IRT has increased in recent years, despite its limited sensitivity and specificity (61% and 74%, respectively). In addition, technological advancements have increased the applicability of IRT [19] . For instance, Ekici and colleagues and Santiago Tello-Mijares used convolutional neural networks (CNNs) to classify breast tissue into normal and abnormal, and the screening accuracy reached 98.95% [18,20] . ...
Background
Given the limited access to breast cancer (BC) screening, the authors developed and validated a mobile phone-artificial intelligence-based infrared thermography (AI-IRT) system for BC screening.
Materials and methods
This large prospective clinical trial assessed the diagnostic performance of the AI-IRT system. The authors constructed two datasets and two models, performed internal and external validation, and compared the diagnostic accuracy of the AI models and clinicians. Dataset A included 2100 patients recruited from 19 medical centres in nine regions of China. Dataset B was used for independent external validation and included 102 patients recruited from Langfang People’s Hospital.
Results
The area under the receiver operating characteristic curve of the binary model for identifying low-risk and intermediate/high-risk patients was 0.9487 (95% CI: 0.9231–0.9744) internally and 0.9120 (95% CI: 0.8460–0.9790) externally. The accuracy of the binary model was higher than that of human readers (0.8627 vs. 0.8088, respectively). In addition, the binary model was better than the multinomial model and used different diagnostic thresholds based on BC risk to achieve specific goals.
Conclusions
The accuracy of AI-IRT was high across populations with different demographic characteristics and less reliant on manual interpretations, demonstrating that this model can improve pre-clinical screening and increase screening rates.
... Infrared sensors operate at wavelengths of 700 nm -1.1 mm, which is longer than visible light (400 -700 nm) [1]. Infrared imagery plays a vital role in military detection [2], surveillance, medical imaging [3,4], oil spill applications [5], and visualization [6,7]. Infrared has the advantage of low scattering and low lighting. ...
... 2. We offer a combined luminance and reflection decomposition and image fusion-based concept. 3. Extensive experiments are conducted on a publicly available dataset to illustrate that the proposed method is competitive with existing infrared image enhancement approaches. ...
... Unlike conventional inspections, which are complicated by the diverse requisite materials and steps, thermal imaging cameras focus only on the relative temperature [22]. Therefore, studies on breast cancer and image diagnosis have con rmed the usefulness of thermal imaging in a healthcare environment by detecting and visualizing temperature differences based on infrared images [23,24]. ...
Background
Particulate matter and infectious diseases confer serious health risks, particularly in healthcare workers who experience occupational exposure risk. Masks can provide effective protection against such risks, although their efficacy is only as good as their fit. Therefore, a fit test is performed to ensure correct fit of the mask. In this study, we aimed to develop an artificial intelligence system to quickly and easily determine correct mask-wearing in real time using thermal videos that ascertained temperature changes caused by air trapped inside the mask.
Methods
We investigated the effectiveness of deep learning-based identification of the correct way to wear a mask based on thermal videos with five types of masks, which were approved as quasi-drugs by the Korean Ministry of Food and Drug Safety, and four ways of wearing these masks including one proper way and three improper ways. The same conditions were repeated five times, with a total of 100 videos per participant, and 5000 videos were obtained in this study. We used a 3D Convolutional Neural Network (3DCNN) and Convolutional Long Short-Term Memory (ConvLSTM) for data analysis. Both models performed binary and multi-classification to categorize mask-wearing.
Results
3DCNN performed better than ConvLSTM by achieving higher scores in both binary and multi-classification tasks. The AUROC value for multi-classification using 3DCNN was the highest at 0.986 whereas the remaining parameters of accuracy, precision, recall, specificity, and F1-score were all better with the binary classification. All mask types showed AUROC values > 0.9, with KF-AD being the best classified.
Conclusion
This novel approach uses thermal imaging and deep learning techniques to effectively monitor correct mask-wearing and could be useful in high-risk environments, including in healthcare settings. This method can be applied to various mask types, which enables easy generalizability and advantages in public and occupational health and healthcare. Furthermore, integrating this novel technology into other screening methods can improve the safety and well-being of people, including healthcare workers, in various situations.
... The Ministry of Health and the National Cancer Institute (Inca) strongly recommend not incorporating thermography into the line of breast diagnostic care. (8)(9)(10)(11)(12) What is the imaging method of choice to guide the biopsy procedure? ...
... The present research study was made in a specialized medical center for breast pathologies (FUCAM A.C.) which allowed the research team to build a large proprietary database of infrared images (20,022 images from 3,337 patients) with respective clinical, radiological, and histopathological patient information. Under the scope of some recent review publication works [31,32], this research is one of the largest combining AI and thermography. ...
Aim: In countries where access to mammography equipment and skilled personnel is limited, most breast cancer (BC) cases are detected in locally advanced stages. Infrared breast thermography is recognized as an adjunctive technique for the detection of BC due to its advantages such as safety (by not emitting ionizing radiation nor applying any stress to the breast), portability, and low cost. Improved by advanced computational analytics techniques, infrared thermography could be a valuable complementary screening technique to detect BC at early stages. In this work, an infrared-artificial intelligence (AI) software was developed and evaluated to help physicians to identify potential BC cases.
Methods: Several AI algorithms were developed and evaluated, which were learned from a proprietary database of 2,700 patients, with BC cases that were confirmed through mammography, ultrasound, and biopsy. Following by evaluation of the algorithms, the best AI algorithm (infrared-AI software) was submitted to a clinic validation process in which its ability to detect BC was compared to mammography evaluations in a double-blind test.
Results: The infrared-AI software demonstrated efficiency values of 94.87% sensitivity, 72.26% specificity, 30.08% positive predictive value (PPV), and 99.12% negative predictive value (NPV), whereas the reference mammography evaluation reached 100% sensitivity, 97.10% specificity, 81.25% PPV, and 100% NPV.
Conclusions: The infrared-AI software here developed shows high BC sensitivity (94.87%) and high NPV (99.12%). Therefore, it is proposed as a complementary screening tool for BC.
