Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks

Fernández-Ovies, Francisco Javier; Alférez-Baquero, Edwin Santiago; Andrés-Galiana, Enrique J. de; Cernea, Ana; Fernández-Muñiz, Zulima; Fernández-Martínez, Juan Luis

doi:10.1007/978-3-030-17935-9_46

Cited by 34 publications

(18 citation statements)

References 10 publications

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“…Baffa and Lattari [23], Fernández-Ovies et al [24], and Tello-Mijares et al [25] use a Convolutional Neural Network to classify patients into healthy and unhealthy using thermal images from the DMR-IR database [26]. Baffa and Lattari [23] used DIT (for final diagnosis) and SIT (for initial screening of cases) and obtained 98% of average accuracy with DIT images and 95% with SIT images.…”

Section: Related Workmentioning

confidence: 99%

“…We compare our results to those studies and summarize such comparisons in Table 14. [20,21], Fernández-Ovies et al [24], Sánchez-Ruiz et al [28], and Silva et al [22,27] do not use the F1-score to assess the classifiers' performance. On the other hand, only Baffa and Lattari [23] and Tello-Mijares et al [25] present an F1-score higher than ours, but both use CNN.…”

Section: Comparison With Related Workmentioning

confidence: 99%

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

Resmini

Silva

Araujo

et al. 2021

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Comparison With Related Workmentioning

confidence: 99%

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

Resmini

Silva

Araujo

et al. 2021

Sensors

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“…Sonuçlar Adam optimizasyon algoritması kullanılarak optimize edilmiştir [43]. Kızılötesi termografiye dayalı olarak meme kanserinin erken teşhisi için karşılaştırmalı olarak ESA performansını inceleyen Fernandez-Ovies ve Andres [44] ResNet18, ResNet34, ResNet50, ResNet152, VGG16 ve VGG19'un ESA mimarileri Fast.ai ve Pytorch kütüphanelerini kullanarak uygulamışlardır. Bu karşılaştırmanın sonucunda, ResNet50'nin %98,75 doğrulukla en iyi sınıflandırmayı sağladığı; ancak ResNet50'nin Res-Net34'e kıyasla daha az kararlı olduğu ifade edilmiştir.…”

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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“…A comparison of the performance of CNN for early detection of breast cancer based on infrared thermography was undertaken previously [124]. CNN architectures of ResNet18, ResNet34, ResNet50, ResNet152, VGG16, and VGG19 were implemented using Fast.ai and Pytorch libraries.…”

Section: Research On Breast Thermogram Classificationmentioning

confidence: 99%

A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection

et al. 2020

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Developing a breast cancer screening method is very important to facilitate early breast cancer detection and treatment. Building a screening method using medical imaging modality that does not cause body tissue damage (non-invasive) and does not involve physical touch is challenging. Thermography, a non-invasive and non-contact cancer screening method, can detect tumors at an early stage even under precancerous conditions by observing temperature distribution in both breasts. The thermograms obtained on thermography can be interpreted using deep learning models such as convolutional neural networks (CNNs). CNNs can automatically classify breast thermograms into categories such as normal and abnormal. Despite their demostrated utility, CNNs have not been widely used in breast thermogram classification. In this study, we aimed to summarize the current work and progress in breast cancer detection based on thermography and CNNs. We first discuss of breast thermography potential in early breast cancer detection, providing an overview of the availability of breast thermal datasets together with publicly accessible. We also discuss characteristics of breast thermograms and the differences between healthy and cancerous thermographic patterns. Breast thermogram classification using a CNN model is described step by step including a simulation example illustrating feature learning. We cover most research related to the implementation of deep neural networks for breast thermogram classification and propose future research directions for developing representative datasets, feeding the segmented image, assigning a good kernel, and building a lightweight CNN model to improve CNN performance. INDEX TERMS breast cancer; convolutional neural network; deep learning; early detection; thermogram

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Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks

Cited by 34 publications

References 10 publications

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

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

Kızılötesi Termal Görüntülemenin Yapay Zeka Tabanlı Biyomedikal Uygulamaları

A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection

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