Computer Aided Diagnosis for Breast Diseases Based on Infrared Images

Araújo, Adriana de; Conci, Aura; Resmini, Roger; Montenegro, Anselmo; Araújo, Claudinéia de; Lebon, Frédéric

doi:10.1109/aiccsa.2017.188

Cited by 14 publications

(4 citation statements)

References 18 publications

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“…A similar approach, using only four clinically significant features, had previously achieved good performance metrics with a larger dataset (ACC = 88.10%, SN = 85.71%, SP = 90.48%) [29]. The Radial Basis Function (RBF) kernel appears to be the best option for the implementation of SVM in breast cancer research, as proven in [30][31][32][33], as well as the use of textural features retrieved from thermograms, all with high performance metrics. Gogoi et al focused its work on the gathering of the best possible inputs that characterize healthy, benign and malignant cases through temperature and intensity analysis (ACC = 83.2%, SN = 85.5%, SP = 73.2%) [34].…”

Section: Breast Cancermentioning

confidence: 89%

“…For future work, most authors emphasize the need for improved feature selection strategies to guarantee the inclusion of significant features, while keeping the number of classification inputs as low as possible, thus reducing processing time [9,15,17,18,29,37,39,41,53,63,65]. To improve classification metrics, IR data could be complemented with information collected from other imaging modalities and/or biological tests [9,26,33,64,70]. The availability of a larger data sample is also mentioned by several studies across the different pathologies, in order to perform more complete testing and ease the implementation of such methodologies in daily practices [9,16,25,26,31,44,46,[52][53][54]58,60,63,65,67].…”

Section: Discussionmentioning

confidence: 99%

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Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

2021

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Atypical body temperature values can be an indication of abnormal physiological processes associated with several health conditions. Infrared thermal (IRT) imaging is an innocuous imaging modality capable of capturing the natural thermal radiation emitted by the skin surface, which is connected to physiology-related pathological states. The implementation of artificial intelligence (AI) methods for interpretation of thermal data can be an interesting solution to supply a second opinion to physicians in a diagnostic/therapeutic assessment scenario. The aim of this work was to perform a systematic review and meta-analysis concerning different biomedical thermal applications in conjunction with machine learning strategies. The bibliographic search yielded 68 records for a qualitative synthesis and 34 for quantitative analysis. The results show potential for the implementation of IRT imaging with AI, but more work is needed to retrieve significant features and improve classification metrics.

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Section: Breast Cancermentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

2021

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“…Araújo et al in [29] developed a methodology to classify breast thermogram images which starts with extracting region of interest using 244 images followed by the extraction of textural features. Using SVM, an accuracy of over 90% is achieved.…”

Section: Acharya Et Al Inmentioning

confidence: 99%

Hybrid Discrete Wavelet Transform and Texture Analysis Methods for Feature Extraction and Classification of Breast Dynamic Thermogram Sequences

Al-Rababah

Mustaffa

Doraisamy

et al. 2021

MJCS

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Breast cancer is a common cancer that hits women causing thousands of casualties every year. A cancerous tumor causes an increase of temperature near the region of the tumor. The heat generated by the temperature transferred to the skin surface. The temperature in the tumor area is warmer than in the healthy area. Detecting breast cancer in early stages can save women’s lives and lower the burden on the cost. Thermography is an imaging technique used for breast cancer detection. A dynamic thermography technique which is used to generate infrared images over a fixed time measured in minutes to detect the difference between the normal and cancerous areas in images. In this research, we propose a methodology to deal with the changes of temperature in patient's breasts by defining a set of efficient features resulted from extraction and reduction of coefficients obtained from breast thermogram images followed by classification. Texture feature methods (Histogram of Oriented Gradients (HOG) and Discrete Curvelet transform) are applied separately using the HH (high-high) and HL (high-low) sub band images of Discrete Wavelet transform (DWT). HOG-based features and Curvelet features are extracted by reducing coefficients’ vectors returned by the two methods. Finally, Support Vector Machine (SVM) binary classifier is used to classify the images to either normal or abnormal. The proposed work has successfully achieved an Accuracy of 98.2%, Sensitivity of 97.7%, and Specificity of 98.2% through empirical studies using dynamic breast thermogram dataset.

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“…Araujo ve ark. meme tümörlerini belirlemek için 60 normal, 66 iyi huylu tümör içeren, 38 kanser dokusu olan toplam 164 meme termogramı ile DVM kullanarak sınıflandırma yapmış ve %90'ın üzerinde doğruluk elde etmiştir [41]. Gogoi ve ark.…”

Section: Meme Kanseriunclassified

Kızılötesi Termal Görüntülemenin Yapay Zeka Tabanlı Biyomedikal Uygulamaları

Ervural

Ceylan

2021

jai

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Kızılötesi termal görüntüleme, vücut sıcaklığı dağılımının ve değişiminin izlenmesine izin veren invazif olmayan, zararlı radyasyon içermeyen, temassız bir modalitedir. Periferik kan akışı, otonom sinir sistemi, vazokonstrüksiyon / vazodilatasyon, iltihaplanma, terleme veya diğer süreçler hakkında fizyolojik bilgiler sağlaması medikal alanda kullanımını yaygınlaştırmıştır. Yapay zekâ alanında yaşanan gelişmeler medikal uygulamalarda da karşılık bulmuş ve makine öğrenimi metotları; karar verme, hastalık takibi, cerrahi planlama gibi birçok görev için kullanılır hâle gelmiştir. Termal verilerin yorumlanması için yapay zekâ yöntemlerinin kullanılması, bir tanı, tedavi planlama veya cerrahi değerlendirme senaryosunda doktorlara ikinci bir görüş sağlamak için etkin bir çözüm olabilir. Bu araştırmanın amacı: Literatür kaynaklarının incelenerek termal görüntülemenin medikal uygulamalardaki sınıflandırma, karar verme gibi süreçlerde yapay zekâ yöntemlerinin işleyişlerini değerlendirmek ve literatür hakkında bilgi sunmaktır.Infrared thermal imaging is a non-invasive, harmless radiation-free non-contact modality that allows monitoring of body temperature distribution and change. Providing physiological information about peripheral blood flow, autonomic nervous system, vasoconstriction / vasodilation, inflammation, sweating or other processes has expanded its use in the medical field. Developments in the field of artificial intelligence have also found response in medical applications, and machine learning methods have become used for many tasks such as decision making, disease monitoring, and surgical planning. Using artificial intelligence methods to interpret thermal data can be an effective solution to provide doctors with a second opinion in a diagnosis, treatment planning or surgical evaluation scenario. The purpose of this research; To evaluate the functioning of artificial intelligence methods in medical applications such as classification and decision making of thermal imaging by examining the literature sources and to provide information about the literature.

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Computer Aided Diagnosis for Breast Diseases Based on Infrared Images

Cited by 14 publications

References 18 publications

Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

Hybrid Discrete Wavelet Transform and Texture Analysis Methods for Feature Extraction and Classification of Breast Dynamic Thermogram Sequences

Kızılötesi Termal Görüntülemenin Yapay Zeka Tabanlı Biyomedikal Uygulamaları

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